| 1. |
- Amundin, Mats, et al.
(author)
-
A proposal to use distributional models to analyse dolphin vocalisation
- 2017
-
In: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 31-32
-
Conference paper (peer-reviewed)abstract
- This paper gives a brief introduction to the starting points of an experimental project to study dolphin communicative behaviour using distributional semantics, with methods implemented for the large scale study of human language.
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| 2. |
- Liu, Yuanhua, 1971, et al.
(author)
-
Considering the importance of user profiles in interface design
- 2009
-
In: User Interfaces. ; , s. 23-
-
Book chapter (other academic/artistic)abstract
- User profile is a popular term widely employed during product design processes by industrial companies. Such a profile is normally intended to represent real users of a product. The ultimate purpose of a user profile is actually to help designers to recognize or learn about the real user by presenting them with a description of a real user’s attributes, for instance; the user’s gender, age, educational level, attitude, technical needs and skill level. The aim of this chapter is to provide information on the current knowledge and research about user profile issues, as well as to emphasize the importance of considering these issues in interface design. In this chapter, we mainly focus on how users’ difference in expertise affects their performance or activity in various interaction contexts. Considering the complex interaction situations in practice, novice and expert users’ interactions with medical user interfaces of different technical complexity will be analyzed as examples: one focuses on novice and expert users’ difference when interacting with simple medical interfaces, and the other focuses on differences when interacting with complex medical interfaces. Four issues will be analyzed and discussed: (1) how novice and expert users differ in terms of performance during the interaction; (2) how novice and expert users differ in the perspective of cognitive mental models during the interaction; (3) how novice and expert users should be defined in practice; and (4) what are the main differences between novice and expert users’ implications for interface design. Besides describing the effect of users’ expertise difference during the interface design process, we will also pinpoint some potential problems for the research on interface design, as well as some future challenges that academic researchers and industrial engineers should face in practice.
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| 3. |
- Schötz, Susanne, et al.
(author)
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Phonetic Characteristics of Domestic Cat Vocalisations
- 2017
-
In: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 5-6
-
Conference paper (peer-reviewed)abstract
- The cat (Felis catus, Linneaus 1758) has lived around or with humans for at least 10,000 years, and is now one of the most popular pets of the world with more than 600 millionindividuals. Domestic cats have developed a more extensive, variable and complex vocal repertoire than most other members of the Carnivora, which may be explained by their social organisation, their nocturnal activity and the long period of association between mother and young. Still, we know surprisingly little about the phonetic characteristics of these sounds, and about the interaction between cats and humans.Members of the research project Melody in human–cat communication (Meowsic) investigate the prosodic characteristics of cat vocalisations as well as the communication between human and cat. The first step includes a categorisation of cat vocalisations. In the next step it will be investigated how humans perceive the vocal signals of domestic cats. This paper presents an outline of the project which has only recently started.
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| 4. |
- Chatterjee, Bapi, 1982
(author)
-
Lock-free Concurrent Search
- 2017
-
Doctoral thesis (other academic/artistic)abstract
- The contemporary computers typically consist of multiple computing cores with high compute power. Such computers make excellent concurrent asynchronous shared memory system. On the other hand, though many celebrated books on data structure and algorithm provide a comprehensive study of sequential search data structures, unfortunately, we do not have such a luxury if concurrency comes in the setting. The present dissertation aims to address this paucity. We describe novel lock-free algorithms for concurrent data structures that target a variety of search problems. (i) Point search (membership query, predecessor query, nearest neighbour query) for 1-dimensional data: Lock-free linked-list; lock-free internal and external binary search trees (BST). (ii) Range search for 1-dimensional data: A range search method for lock-free ordered set data structures - linked-list, skip-list and BST. (iii) Point search for multi-dimensional data: Lock-free kD-tree, specially, a generic method for nearest neighbour search. We prove that the presented algorithms are linearizable i.e. the concurrent data structure operations intuitively display their sequential behaviour to an observer of the concurrent system. The lock-freedom in the introduced algorithms guarantee overall progress in an asynchronous shared memory system. We present the amortized analysis of lock-free data structures to show their efficiency. Moreover, we provide sample implementations of the algorithms and test them over extensive micro-benchmarks. Our experiments demonstrate that the implementations are scalable and perform well when compared to related existing alternative implementations on common multi-core computers. Our focus is on propounding the generic methodologies for efficient lock-free concurrent search. In this direction, we present the notion of help-optimality, which captures the optimization of amortized step complexity of the operations. In addition to that, we explore the language-portable design of lock-free data structures that aims to simplify an implementation from programmer’s point of view. Finally, our techniques to implement lock-free linearizable range search and nearest neighbour search are independent of the underlying data structures and thus are adaptive to similar data structures.
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| 5. |
- Lindgren, Helena, Professor, et al.
(author)
-
The wasp-ed AI curriculum : A holistic curriculum for artificial intelligence
- 2023
-
In: INTED2023 Proceedings. - : IATED. - 9788409490264 ; , s. 6496-6502
-
Conference paper (peer-reviewed)abstract
- Efforts in lifelong learning and competence development in Artificial Intelligence (AI) have been on the rise for several years. These initiatives have mostly been applied to Science, Technology, Engineering and Mathematics (STEM) disciplines. Even though there has been significant development in Digital Humanities to incorporate AI methods and tools in higher education, the potential for such competences in Arts, Humanities and Social Sciences is far from being realised. Furthermore, there is an increasing awareness that the STEM disciplines need to include competences relating to AI in humanity and society. This is especially important considering the widening and deepening of the impact of AI on society at large and individuals. The aim of the presented work is to provide a broad and inclusive AI Curriculum that covers the breadth of the topic as it is seen today, which is significantly different from only a decade ago. It is important to note that with the curriculum we mean an overview of the subject itself, rather than a particular education program. The curriculum is intended to be used as a foundation for educational activities in AI to for example harmonize terminology, compare different programs, and identify educational gaps to be filled. An important aspect of the curriculum is the ethical, legal, and societal aspects of AI and to not limit the curriculum to the STEM subjects, instead extending to a holistic, human-centred AI perspective. The curriculum is developed as part of the national research program WASP-ED, the Wallenberg AI and transformative technologies education development program.
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| 6. |
- Yun, Yixiao, 1987, et al.
(author)
-
Maximum-Likelihood Object Tracking from Multi-View Video by Combining Homography and Epipolar Constraints
- 2012
-
In: 6th ACM/IEEE Int'l Conf on Distributed Smart Cameras (ICDSC 12), Oct 30 - Nov.2, 2012, Hong Kong. - 9781450317726 ; , s. 6 pages-
-
Conference paper (peer-reviewed)abstract
- This paper addresses problem of object tracking in occlusion scenarios, where multiple uncalibrated cameras with overlapping fields of view are used. We propose a novel method where tracking is first done independently for each view and then tracking results are mapped between each pair of views to improve the tracking in individual views, under the assumptions that objects are not occluded in all views and move uprightly on a planar ground which may induce a homography relation between each pair of views. The tracking results are mapped by jointly exploiting the geometric constraints of homography, epipolar and vertical vanishing point. Main contributions of this paper include: (a) formulate a reference model of multi-view object appearance using region covariance for each view; (b) define a likelihood measure based on geodesics on a Riemannian manifold that is consistent with the destination view by mapping both the estimated positions and appearances of tracked object from other views; (c) locate object in each individual view based on maximum likelihood criterion from multi-view estimations of object position. Experiments have been conducted on videos from multiple uncalibrated cameras, where targets experience long-term partial or full occlusions. Comparison with two existing methods and performance evaluations are also made. Test results have shown effectiveness of the proposed method in terms of robustness against tracking drifts caused by occlusions.
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| 7. |
- Ge, Chenjie, 1991, et al.
(author)
-
Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare
- 2018
-
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; 2018-July, s. 1572-1575
-
Conference paper (peer-reviewed)abstract
- This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
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| 8. |
- Blanch, Krister, 1991
(author)
-
Beyond-application datasets and automated fair benchmarking
- 2023
-
Licentiate thesis (other academic/artistic)abstract
- Beyond-application perception datasets are generalised datasets that emphasise the fundamental components of good machine perception data. When analysing the history of perception datatsets, notable trends suggest that design of the dataset typically aligns with an application goal. Instead of focusing on a specific application, beyond-application datasets instead look at capturing high-quality, high-volume data from a highly kinematic environment, for the purpose of aiding algorithm development and testing in general. Algorithm benchmarking is a cornerstone of autonomous systems development, and allows developers to demonstrate their results in a comparative manner. However, most benchmarking systems allow developers to use their own hardware or select favourable data. There is also little focus on run time performance and consistency, with benchmarking systems instead showcasing algorithm accuracy. By combining both beyond-application dataset generation and methods for fair benchmarking, there is also the dilemma of how to provide the dataset to developers for this benchmarking, as the result of a high-volume, high-quality dataset generation is a significant increase in dataset size when compared to traditional perception datasets. This thesis presents the first results of attempting the creation of such a dataset. The dataset was built using a maritime platform, selected due to the highly dynamic environment presented on water. The design and initial testing of this platform is detailed, as well as as methods of sensor validation. Continuing, the thesis then presents a method of fair benchmarking, by utilising remote containerisation in a way that allows developers to present their software to the dataset, instead of having to first locally store a copy. To test this dataset and automatic online benchmarking, a number of reference algorithms were required for initial results. Three algorithms were built, using the data from three different sensors captured on the maritime platform. Each algorithm calculates vessel odometry, and the automatic benchmarking system was utilised to show the accuracy and run-time performance of these algorithms. It was found that the containerised approach alleviated data management concerns, prevented inflated accuracy results, and demonstrated precisely how computationally intensive each algorithm was.
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| 9. |
- Suchan, Jakob, et al.
(author)
-
Commonsense Visual Sensemaking for Autonomous Driving : On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics
- 2021
-
In: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 299
-
Journal article (peer-reviewed)abstract
- We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming (ASP) is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control. We evaluate and demonstrate with community established benchmarks KITTIMOD, MOT-2017, and MOT-2020. As use-case, we focus on the significance of human-centred visual sensemaking —e.g., involving semantic representation and explainability, question-answering, commonsense interpolation— in safety-critical autonomous driving situations. The developed neurosymbolic framework is domain-independent, with the case of autonomous driving designed to serve as an exemplar for online visual sensemaking in diverse cognitive interaction settings in the backdrop of select human-centred AI technology design considerations.
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| 10. |
- Nguyen, Björnborg, 1992, et al.
(author)
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Systematic benchmarking for reproducibility of computer vision algorithms for real-time systems: The example of optic flow estimation
- 2019
-
In: IEEE International Conference on Intelligent Robots and Systems. - : IEEE. - 2153-0858 .- 2153-0866. ; , s. 5264-5269
-
Conference paper (peer-reviewed)abstract
- Until now there have been few formalized methods for conducting systematic benchmarking aiming at reproducible results when it comes to computer vision algorithms. This is evident from lists of algorithms submitted to prominent datasets, authors of a novel method in many cases primarily state the performance of their algorithms in relation to a shallow description of the hardware system where it was evaluated. There are significant problems linked to this non-systematic approach of reporting performance, especially when comparing different approaches and when it comes to the reproducibility of claimed results. Furthermore how to conduct retrospective performance analysis such as an algorithm's suitability for embedded real-time systems over time with underlying hardware and software changes in place. This paper proposes and demonstrates a systematic way of addressing such challenges by adopting containerization of software aiming at formalization and reproducibility of benchmarks. Our results show maintainers of broadly accepted datasets in the computer vision community to strive for systematic comparison and reproducibility of submissions to increase the value and adoption of computer vision algorithms in the future.
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| 11. |
- Ali, Muhaddisa Barat, 1986
(author)
-
Deep Learning Methods for Classification of Gliomas and Their Molecular Subtypes, From Central Learning to Federated Learning
- 2023
-
Doctoral thesis (other academic/artistic)abstract
- The most common type of brain cancer in adults are gliomas. Under the updated 2016 World Health Organization (WHO) tumor classification in central nervous system (CNS), identification of molecular subtypes of gliomas is important. For low grade gliomas (LGGs), prediction of molecular subtypes by observing magnetic resonance imaging (MRI) scans might be difficult without taking biopsy. With the development of machine learning (ML) methods such as deep learning (DL), molecular based classification methods have shown promising results from MRI scans that may assist clinicians for prognosis and deciding on a treatment strategy. However, DL requires large amount of training datasets with tumor class labels and tumor boundary annotations. Manual annotation of tumor boundary is a time consuming and expensive process. The thesis is based on the work developed in five papers on gliomas and their molecular subtypes. We propose novel methods that provide improved performance. The proposed methods consist of a multi-stream convolutional autoencoder (CAE)-based classifier, a deep convolutional generative adversarial network (DCGAN) to enlarge the training dataset, a CycleGAN to handle domain shift, a novel federated learning (FL) scheme to allow local client-based training with dataset protection, and employing bounding boxes to MRIs when tumor boundary annotations are not available. Experimental results showed that DCGAN generated MRIs have enlarged the original training dataset size and have improved the classification performance on test sets. CycleGAN showed good domain adaptation on multiple source datasets and improved the classification performance. The proposed FL scheme showed a slightly degraded performance as compare to that of central learning (CL) approach while protecting dataset privacy. Using tumor bounding boxes showed to be an alternative approach to tumor boundary annotation for tumor classification and segmentation, with a trade-off between a slight decrease in performance and saving time in manual marking by clinicians. The proposed methods may benefit the future research in bringing DL tools into clinical practice for assisting tumor diagnosis and help the decision making process.
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| 12. |
- Kavathatzopoulos, Iordanis, 1956-
(author)
-
Robots and systems as autonomous ethical agents
- 2010
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In: INTECH 2010. - Bangkok : Assumption University. - 9789746151108 ; , s. 5-9
-
Conference paper (peer-reviewed)abstract
- IT systems and robots can help us to solve many problems caused by the quantity, variation and complexity of information; because we need to handle dangerous and risky situations; or because of our social and emotional needs like elderly care. In helping us, these systems have to make decisions and act accordingly to achieve the goals for which they were built. Ethical decision support tools can be integrated into robots and other decision making systems to secure that decisions are made according to the basic theories of philosophy and to the findings of psychological research. This can be done, in non-independent systems, as a way for the system to report to its operator, and to support the operator's ethical decision making. On the other hand, fully independent systems should be able to regulate their own decision making strategies and processes. However, this cannot be based on normative predefined criteria, or on the ability to make choices, or on having own control, or on ability of rational processing. It seems that it is necessary for an independent robot or decision system to have "emotions." That is, a kind of ultimate purposes that can lead the decision process, and depending on the circumstances, guide the adoption of a decision strategy, whatever it may be, rational, heuristic or automatic.
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| 13. |
- Jacobsson, Martin, 1976-, et al.
(author)
-
A Drone-mounted Depth Camera-based Motion Capture System for Sports Performance Analysis
- 2023
-
In: Artificial Intelligence in HCI. - : Springer Nature. - 9783031358937 ; , s. 489-503
-
Conference paper (peer-reviewed)abstract
- Video is the most used tool for sport performance analysis as it provides a common reference point for the coach and the athlete. The problem with video is that it is a subjective tool. To overcome this, motion capture systems can used to get an objective 3D model of a per- son’s posture and motion, but only in laboratory settings. Unfortunately, many activities, such as most outdoor sports, cannot be captured in a lab without compromising the activity. In this paper, we propose to use an aerial drone system equipped with depth cameras, AI-based marker- less motion capture software to perform automatic skeleton tracking and real-time sports performance analysis of athletes. We experiment with off-the-shelf drone systems, miniaturized depth cameras, and commer- cially available skeleton tracking software to build a system for analyzing sports-related performance of athletes in their real settings. To make this a fully working system, we have conducted a few initial experiments and identified many issues that still needs to be addressed.
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| 14. |
- Frid, Emma, 1988-, et al.
(author)
-
Perceptual Evaluation of Blended Sonification of Mechanical Robot Sounds Produced by Emotionally Expressive Gestures : Augmenting Consequential Sounds to Improve Non-verbal Robot Communication
- 2021
-
In: International Journal of Social Robotics. - : Springer Nature. - 1875-4791 .- 1875-4805.
-
Journal article (peer-reviewed)abstract
- This paper presents two experiments focusing on perception of mechanical sounds produced by expressive robot movement and blended sonifications thereof. In the first experiment, 31 participants evaluated emotions conveyed by robot sounds through free-form text descriptions. The sounds were inherently produced by the movements of a NAO robot and were not specifically designed for communicative purposes. Results suggested no strong coupling between the emotional expression of gestures and how sounds inherent to these movements were perceived by listeners; joyful gestures did not necessarily result in joyful sounds. A word that reoccurred in text descriptions of all sounds, regardless of the nature of the expressive gesture, was “stress”. In the second experiment, blended sonification was used to enhance and further clarify the emotional expression of the robot sounds evaluated in the first experiment. Analysis of quantitative ratings of 30 participants revealed that the blended sonification successfully contributed to enhancement of the emotional message for sound models designed to convey frustration and joy. Our findings suggest that blended sonification guided by perceptual research on emotion in speech and music can successfully improve communication of emotions through robot sounds in auditory-only conditions.
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| 15. |
- Koriakina, Nadezhda, 1991-, et al.
(author)
-
Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
- 2024
-
In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
-
Journal article (peer-reviewed)abstract
- The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
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| 16. |
- Lv, Zhihan, Dr. 1984-, et al.
(author)
-
Editorial : 5G for Augmented Reality
- 2022
-
In: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153.
-
Journal article (peer-reviewed)
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| 17. |
- Romero, Mario, 1973-, et al.
(author)
-
Alien Presence in the Home : The Design of Tableau Machine
- 2008
-
In: Personal and Ubiquitous Computing. - : Springer. - 1617-4909 .- 1617-4917. ; 12:5, s. 373-382
-
Journal article (peer-reviewed)abstract
- We introduce a design strategy, alien presence, which combines work in human---computer interaction, artificial intelligence, and media art to create enchanting experiences involving reflection over and contemplation of daily activities. An alien presence actively interprets and characterizes daily activity and reflects it back via generative, ambient displays that avoid simple one-to-one mappings between sensed data and output. We describe the alien presence design strategy for achieving enchantment, and report on Tableau Machine, a concrete example of an alien presence design for domestic spaces. We report on an encouraging formative evaluation indicating that Tableau Machine does indeed support reflection and actively engages users in the co-construction of meaning around the display.
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| 18. |
- Norlund, Tobias, 1991, et al.
(author)
-
Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it?
- 2021
-
In: Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 149-162, Punta Cana, Dominican Republic. - : Association for Computational Linguistics.
-
Conference paper (peer-reviewed)abstract
- Large language models are known to suffer from the hallucination problem in that they are prone to output statements that are false or inconsistent, indicating a lack of knowledge. A proposed solution to this is to provide the model with additional data modalities that complements the knowledge obtained through text. We investigate the use of visual data to complement the knowledge of large language models by proposing a method for evaluating visual knowledge transfer to text for uni- or multimodal language models. The method is based on two steps, 1) a novel task querying for knowledge of memory colors, i.e. typical colors of well-known objects, and 2) filtering of model training data to clearly separate knowledge contributions. Additionally, we introduce a model architecture that involves a visual imagination step and evaluate it with our proposed method. We find that our method can successfully be used to measure visual knowledge transfer capabilities in models and that our novel model architecture shows promising results for leveraging multimodal knowledge in a unimodal setting.
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| 19. |
- Shirabe, Takeshi
(author)
-
Drawing with geography
- 2015
-
In: Lecture Notes in Geoinformation and Cartography. - Cham : Springer International Publishing. - 9783319167862 ; , s. 327-341
-
Conference paper (peer-reviewed)abstract
- A method is proposed to assist spatial planners in drawing with ‘geographic’ constraints. These constraints constrain graphic objects to have certain relationships that are not limited to be (Euclidean) geometric or topological but allowed to be dependent on the spatial variation of selected conditions (e.g., elevation and vegetation) characterizing an underlying geographic space. Just as in existing computer-aided design systems, the method accepts a manual change to a graphic object or constraint, and updates all affected graphic objects accordingly. The paper discusses how such a method is motivated and improves the graphic editing capability of geographic information systems, and identifies key issues for its implementation.
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| 20. |
- Hedenberg, Klas, 1968-, et al.
(author)
-
Obstacle Detection For Thin Horizontal Structures
- 2008
-
In: World Congress on Engineering and Computer Science. - Hong Kong : International Association of Engineers. - 9789889867102 ; , s. 689-693
-
Conference paper (peer-reviewed)abstract
- Many vision-based approaches for obstacle detection often state that vertical thin structure is of importance, e.g. poles and trees. However, there are also problem in detecting thin horizontal structures. In an industrial case there are horizontal objects, e.g. cables and fork lifts, and slanting objects, e.g. ladders, that also has to be detected. This paper focuses on the problem to detect thin horizontal structures. The system uses three cameras, situated as a horizontal pair and a vertical pair, which makes it possible to also detect thin horizontal structures. A comparison between a sparse disparity map based on edges and a dense disparity map with a column and row filter is made. Both methods use the Sum of Absolute Difference to compute the disparity maps. Special interest has been in scenes with thin horizontal objects. Tests show that the sparse dense method based on the Canny edge detector works better for the environments we have tested.
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| 21. |
- Kavathatzopoulos, Iordanis, 1956-, et al.
(author)
-
What are ethical agents and how can we make them work properly?
- 2011
-
In: The computational turn. - Münster : MV-Wissenschaft. - 9783869913551 ; , s. 151-153
-
Conference paper (peer-reviewed)abstract
- To support ethical decision making in autonomous agents, we suggest to implement decision tools based on classical philosophy and psychological research. As one possible avenue, we present EthXpert, which supports the process of structuring and assembling information about situations with possible moral implications.
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| 22. |
- Patrignani, Norberto
(author)
-
From computer ethics to future (and information) ethics : The challenge of Nano-Bots
- 2014
-
In: Ethical dimensions of bio-nanotechnology. - Hershey, PA, USA : IGI Global. - 9781466618947
-
Book chapter (peer-reviewed)abstract
- One of the emerging technologies that is getting a lot of attention is nano-technology. In particular,in this area, the convergence of research fields of biotechnology, information technology, nanotechnologyand neuroscience (or cognitive science) is introducing nano-robots, or nano-bots. These machines promise to lead to the development of a large number potential applications in medicine,but at the same time they raise also a lot of social and ethical issues. This chapter introduces severalways to start an ethical reflection in relation to nano-bots. The traditional "computer ethics"approach and the new "future ethics" proposition are both discussed and applied to this technology.The challenges introduced by nano-bots are so complex that it is possible that the application of thePrecautionary Principle would be required. A further ethical analysis of nano-bots applications inmedicine may benefit from new methodologies and strategies such as the stakeholders' network andFloridi's "entropy (the evil of Infosphere)" concept.
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| 23. |
- Rumman, Nadine Abu, et al.
(author)
-
Skin deformation methods for interactive character animation
- 2017
-
In: Communications in Computer and Information Science. - Cham : Springer International Publishing. - 1865-0937 .- 1865-0929. ; 693, s. 153-174, s. 153-174
-
Conference paper (peer-reviewed)abstract
- Character animation is a vital component of contemporary computer games, animated feature films and virtual reality applications. The problem of creating appealing character animation can best be described by the title of the animation bible: “The Illusion of Life”. The focus is not on completing a given motion task, but more importantly on how this motion task is performed by the character. This does not necessarily require realistic behavior, but behavior that is believable. This of course includes the skin deformations when the character is moving. In this paper, we focus on the existing research in the area of skin deformation, ranging from skeleton-based deformation and volume preserving techniques to physically based skinning methods. We also summarize the recent contributions in deformable and soft body simulations for articulated characters, and discuss various geometric and example-based approaches. © Springer International Publishing AG 2017.
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| 24. |
- Ghielmetti, N., et al.
(author)
-
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
- 2022
-
In: Machine Learning - Science and Technology. - : IOP Publishing. - 2632-2153. ; 3:4
-
Journal article (peer-reviewed)abstract
- In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.
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| 25. |
- Bhatt, Mehul, Professor, 1980-, et al.
(author)
-
Cognitive Vision and Perception : Deep Semantics Integrating AI and Vision for Reasoning about Space, Motion, and Interaction
- 2020
-
In: ECAI 2020. - : IOS Press. - 9781643681009 - 9781643681016 ; , s. 2881-2882
-
Conference paper (peer-reviewed)abstract
- Semantic interpretation of dynamic visuospatial imagery calls for a general and systematic integration of methods in knowledge representation and computer vision. Towards this, we highlight research articulating & developing deep semantics, characterised by the existence of declarative models –e.g., pertaining space and motion– and corresponding formalisation and reasoning methods sup- porting capabilities such as semantic question-answering, relational visuospatial learning, and (non-monotonic) visuospatial explanation. We position a working model for deep semantics by highlighting select recent / closely related works from IJCAI, AAAI, ILP, and ACS. We posit that human-centred, explainable visual sensemaking necessitates both high-level semantics and low-level visual computing, with the highlighted works providing a model for systematic, modular integration of diverse multifaceted techniques developed in AI, ML, and Computer Vision.
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| 26. |
- Haage, Mathias, et al.
(author)
-
Teaching Assembly by Demonstration using Advanced Human Robot Interaction and a Knowledge Integration Framework
- 2017
-
In: Procedia Manufacturing. - : Elsevier BV. - 2351-9789. ; 11, s. 164-173
-
Journal article (peer-reviewed)abstract
- Conventional industrial robots are heavily dependent on hard automation that requires pre-specified fixtures and time-consuming (re)programming performed by experienced operators. In this work, teaching by human-only demonstration is used for reducing required time and expertise to setup a robotized assembly station. This is achieved by the proposed framework enhancing the robotic system with advanced perception and cognitive abilities, accessed through a user-friendly Human Robot Interaction interface. The approach is evaluated on a small parts’ assembly use case deployed onto a collaborative industrial robot testbed. Experiments indicate that the proposed approach allows inexperienced users to efficiently teach robots new assembly tasks.
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| 27. |
- Lv, Zhihan, Dr. 1984-, et al.
(author)
-
5G for mobile augmented reality
- 2022
-
In: International Journal of Communication Systems. - : John Wiley & Sons. - 1074-5351 .- 1099-1131. ; 35:5
-
Journal article (other academic/artistic)
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| 28. |
- Frid, Emma, et al.
(author)
-
Perception of Mechanical Sounds Inherent to Expressive Gestures of a NAO Robot - Implications for Movement Sonification of Humanoids
- 2018
-
In: Proceedings of the 15th Sound and Music Computing Conference. - Limassol, Cyprus. - 9789963697304
-
Conference paper (peer-reviewed)abstract
- In this paper we present a pilot study carried out within the project SONAO. The SONAO project aims to compen- sate for limitations in robot communicative channels with an increased clarity of Non-Verbal Communication (NVC) through expressive gestures and non-verbal sounds. More specifically, the purpose of the project is to use move- ment sonification of expressive robot gestures to improve Human-Robot Interaction (HRI). The pilot study described in this paper focuses on mechanical robot sounds, i.e. sounds that have not been specifically designed for HRI but are inherent to robot movement. Results indicated a low correspondence between perceptual ratings of mechanical robot sounds and emotions communicated through ges- tures. In general, the mechanical sounds themselves ap- peared not to carry much emotional information compared to video stimuli of expressive gestures. However, some mechanical sounds did communicate certain emotions, e.g. frustration. In general, the sounds appeared to commu- nicate arousal more effectively than valence. We discuss potential issues and possibilities for the sonification of ex- pressive robot gestures and the role of mechanical sounds in such a context. Emphasis is put on the need to mask or alter sounds inherent to robot movement, using for exam- ple blended sonification.
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| 29. |
- Menghi, Claudio, 1987, et al.
(author)
-
Poster: Property specification patterns for robotic missions
- 2018
-
In: Proceedings - International Conference on Software Engineering. - New York, NY, USA : ACM. - 0270-5257. ; Part F137351, s. 434-435
-
Conference paper (peer-reviewed)abstract
- Engineering dependable software for mobile robots is becoming increasingly important. A core asset in engineering mobile robots is the mission specification-A formal description of the goals that mobile robots shall achieve. Such mission specifications are used, among others, to synthesize, verify, simulate, or guide the engineering of robot software. Development of precise mission specifications is challenging. Engineers need to translate the mission requirements into specification structures expressed in a logical language-A laborious and error-prone task. To mitigate this problem, we present a catalog of mission specification patterns for mobile robots. Our focus is on robot movement, one of the most prominent and recurrent specification problems for mobile robots. Our catalog maps common mission specification problems to recurrent solutions, which we provide as templates that can be used by engineers. The patterns are the result of analyzing missions extracted from the literature. For each pattern, we describe usage intent, known uses, relationships to other patterns, and-most importantly-A template representing the solution as a logical formula in temporal logic. Our specification patterns constitute reusable building blocks that can be used by engineers to create complex mission specifications while reducing specification mistakes. We believe that our patterns support researchers working on tool support and techniques to synthesize and verify mission specifications, and language designers creating rich domain-specific languages for mobile robots, incorporating our patterns as language concepts.
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| 30. |
- Fu, Keren, et al.
(author)
-
Deepside: A general deep framework for salient object detection
- 2019
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 356, s. 69-82
-
Journal article (peer-reviewed)abstract
- Deep learning-based salient object detection techniques have shown impressive results compared to con- ventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improve- ment on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side struc- tures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
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| 31. |
- Lv, Zhihan, Dr. 1984-, et al.
(author)
-
Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems
- 2022
-
In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16666-16675
-
Journal article (peer-reviewed)abstract
- The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy's effects become more apparent. When MPR ranges between 40% similar to 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.
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| 32. |
-
Computer Analysis of Images and Patterns : 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II
- 2017
-
Editorial proceedings (peer-reviewed)abstract
- The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in August 2017. The 72 papers presented were carefully reviewed and selected from 144 submissions The papers are organized in the following topical sections: Vision for Robotics; Motion and Tracking; Segmentation; Image/Video Indexing and Retrieval; Shape Representation and Analysis; Biomedical Image Analysis; Biometrics; Machine Learning; Image Restoration; and Poster Sessions.
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| 33. |
- Pousman, Zachary, et al.
(author)
-
Living with Tableau Machine : A Longitudinal Investigation of a Curious Domestic Intelligence
- 2008
-
In: Proceedings of the 10th International Conference on Ubiquitous Computing. - New York, NY, USA : ACM Press. ; , s. 370-379
-
Conference paper (peer-reviewed)abstract
- We present a longitudinal investigation of Tableau Machine, an intelligent entity that interprets and reflects the lives of occupants in the home. We created Tableau Machine (TM) to explore the parts of home life that are unrelated to accomplishing tasks. Task support for "smart homes" has inspired many researchers in the community. We consider design for experience, an orthogonal dimension to task-centric home life. TM produces abstract visualizations on a large LCD every few minutes, driven by a set of four overhead cameras that capture a sense of the social life of a domestic space. The openness and ambiguity of TM allow for a cycle of co-interpretation with householders. We report on three longitudinal deployments of TM for a period of six weeks. Participant families engaged with TM at the outset to understand how their behaviors were influencing the machine, and, while TM remained puzzling, householders interacted richly with TM and its images. We extract some key design implications for an experience-focused smart home.
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| 34. |
- Singh, Avinash, 1986-, et al.
(author)
-
Verbal explanations by collaborating robot teams
- 2021
-
In: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 12:1, s. 47-57
-
Journal article (peer-reviewed)abstract
- In this article, we present work on collaborating robot teams that use verbal explanations of their actions and intentions in order to be more understandable to the human. For this, we introduce a mechanism that determines what information the robots should verbalize in accordance with Grice’s maxim of quantity, i.e., convey as much information as is required and no more or less. Our setup is a robot team collaborating to achieve a common goal while explaining in natural language what they are currently doing and what they intend to do. The proposed approach is implemented on three Pepper robots moving objects on a table. It is evaluated by human subjects answering a range of questions about the robots’ explanations, which are generated using either our proposed approach or two further approaches implemented for evaluation purposes. Overall, we find that our proposed approach leads to the most understanding of what the robots are doing. In addition, we further propose a method for incorporating policies driving the distribution of tasks among the robots, which may further support understandability.
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| 35. |
- Svärd, Malin, 1985
(author)
-
Computational driver behavior models for vehicle safety applications
- 2023
-
Doctoral thesis (other academic/artistic)abstract
- The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision. First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles. Moreover, a basic model for drivers' brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance's visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections. Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers' recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road. To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic.
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| 36. |
- Daoud, Adel, 1981, et al.
(author)
-
Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
- 2023
-
In: Social Indicators Research. - : SPRINGER. - 0303-8300 .- 1573-0921. ; 167:1-3, s. 475-505
-
Journal article (peer-reviewed)abstract
- Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables—over 90 variables—included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.
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| 37. |
- Tanqueray, Laetitia, et al.
(author)
-
Gender Fairness in Social Robotics : Exploring a Future Care of Peripartum Depression
- 2022
-
In: Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction : Alt.HRI - Our Robotics Futures: A Time Capsule - Alt.HRI - Our Robotics Futures: A Time Capsule. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538685549 ; , s. 598-607, s. 598-607
-
Conference paper (peer-reviewed)abstract
- In this paper we investigate the possibility of socially assistive robots (SARs) supporting diagnostic screening for peripartum depression (PPD) within the next five years. Through a HRI/socio-legal collaboration, we explore the gender norms within PPD in Sweden, to inform a gender-sensitive approach to designing SARs in such a setting, as well as governance implications. This is achieved through conducting expert interviews and qualitatively analysing the data. Based on the results, we conclude that a gender-sensitive approach is a necessity in relation to the design and governance of SARs for PPD screening.
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| 38. |
- Frintrop, Simone
(author)
-
VOCUS : A visual attention system for object detection and goal-directed search
- 2006
-
In: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349. - 3540327592 - 9783540327592 ; , s. 1-228
-
Journal article (peer-reviewed)abstract
- Visual attention is a mechanism in human perception which selects relevant regions from a scene and provides these regions for higher-level processing as object recognition. This enables humans to act effectively in their environment despite the complexity of perceivable sensor data. Computational vision systems face the same problem as humans: there is a large amount of information to be processed and to achieve this efficiently, maybe even in real-time for robotic applications, the order in which a scene is investigated must be determined in an intelligent way. A promising approach is to use computational attention systems that simulate human visual attention. This monograph introduces the biologically motivated computational attention system VOCUS (Visual Object detection with a Computational attention System) that detects regions of interest in images. It operates in two modes, in an exploration mode in which no task is provided, and in a search mode with a specified target. In exploration mode, regions of interest are defined by strong contrasts (e.g., color or intensity contrasts) and by the uniqueness of a feature. For example, a black sheep is salient in a flock of white sheep. In search mode, the system uses previously learned information about a target object to bias the saliency computations with respect to the target. In various experiments, it is shown that the target is on average found with less than three fixations, that usually less than five training images suffice to learn the target information, and that the system is mostly robust with regard to viewpoint changes and illumination variances. Furthermore, we demonstrate how VOCUS profits from additional sensor data: we apply the system to depth and reflectance data from a 3D laser scanner and show the advantages that the laser modes provide. By fusing the data of both modes, we demonstrate how the system is able to consider distinct object properties and how the flexibility of the system increases by considering different data. Finally, the regions of interest provided by VOCUS serve as input to a classifier that recognizes the object in the detected region. We show how and in which cases the classification is sped up and how the detection quality is improved by the attentional front-end. This approach is especially useful if many object classes have to be considered, a frequently occurring situation in robotics. VOCUS provides a powerful approach to improve existing vision systems by concentrating computational resources to regions that are more likely to contain relevant information. The more the complexity and power of vision systems increase in the future, the more they will profit from an attentional front-end like VOCUS.
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| 39. |
- Gu, Irene Yu-Hua, 1953, et al.
(author)
-
Grassmann Manifold Online Learning and Partial Occlusion Handling for Visual Object Tracking under Bayesian Formulation
- 2012
-
In: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9784990644109 ; , s. 1463-1466
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues of online learning and occlusion handling in video object tracking. Although manifold tracking is promising, large pose changes and long term partial occlusions of video objects remain challenging.We propose a novel manifold tracking scheme that tackles such problems, with the following main novelties: (a) Online estimation of object appearances on Grassmann manifolds; (b) Optimal criterion-based occlusion handling during online learning; (c) Nonlinear dynamic model for appearance basis matrix and its velocity; (b) Bayesian formulations separately for the tracking and the online learning process. Two particle filters are employed: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in alternative fashion to mitigate the tracking drift. Experiments on videos have shown robust tracking performance especially when objects contain significantpose changes accompanied with long-term partial occlusions. Evaluations and comparisons with two existing methods provide further support to the proposed method.
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| 40. |
- Kurtser, Polina, 1990-, et al.
(author)
-
RGB-D datasets for robotic perception in site-specific agricultural operations : a survey
- 2023
-
In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 212
-
Journal article (peer-reviewed)abstract
- Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.
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| 41. |
- Lindeberg, Tony, 1964-, et al.
(author)
-
Analysis of brain activation patterns using a 3-D scale-space primal sketch
- 1999
-
In: Human Brain Mapping. - 1065-9471 .- 1097-0193. ; 7:3, s. 166-94
-
Journal article (peer-reviewed)abstract
- A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale-space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale-space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data.
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| 42. |
- Gerken, Jan, 1991, et al.
(author)
-
Equivariance versus augmentation for spherical images
- 2022
-
In: Proceedings of Machine Learning Resaerch. ; , s. 7404-7421
-
Conference paper (peer-reviewed)abstract
- We analyze the role of rotational equivariance in convolutional neural networks (CNNs) applied to spherical images. We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation. The chosen architectures can be considered baseline references for the respective design paradigms. Our models are trained and evaluated on single or multiple items from the MNIST- or FashionMNIST dataset projected onto the sphere. For the task of image classification, which is inherently rotationally invariant, we find that by considerably increasing the amount of data augmentation and the size of the networks, it is possible for the standard CNNs to reach at least the same performance as the equivariant network. In contrast, for the inherently equivariant task of semantic segmentation, the non-equivariant networks are consistently outperformed by the equivariant networks with significantly fewer parameters. We also analyze and compare the inference latency and training times of the different networks, enabling detailed tradeoff considerations between equivariant architectures and data augmentation for practical problems.
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| 43. |
- Bouguerra, Abdelbaki, 1974-, et al.
(author)
-
An autonomous robotic system for load transportation
- 2009
-
In: 2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009). - New York : IEEE conference proceedings. - 9781424427277 - 9781424427284 ; , s. 1563-1566
-
Conference paper (peer-reviewed)abstract
- This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems.
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| 44. |
- Benderius, Ola, 1985, et al.
(author)
-
The Best Rated Human-Machine Interface Design for Autonomous Vehicles in the 2016 Grand Cooperative Driving Challenge
- 2018
-
In: IEEE transactions on intelligent transportation systems (Print). - 1524-9050 .- 1558-0016. ; 19:4, s. 1302-1307
-
Journal article (peer-reviewed)abstract
- This paper provides an in-depth description of the best rated human-machine interface that was presented during the 2016 Grand Cooperative Driving Challenge. It was demonstrated by the Chalmers Truck Team as the envisioned interface to their open source software framework OpenDLV, which is used to power Chalmers' fleet of self-driving vehicles. The design originates from the postulate that the vehicle is fully autonomous to handle even complex traffic scenarios. Thus, by including external and internal interfaces, and introducing a show, don't tell principle, it aims at fulfilling the needs of the vehicle occupants as well as other participants in the traffic environment. The design also attempts to comply with, and slightly extend, the current traffic rules and legislation for the purpose of being realistic for full-scale implementation.
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| 45. |
- Liu, Yang, et al.
(author)
-
Movement Status Based Vision Filter for RoboCup Small-Size League
- 2012
-
In: Advances in Automation and Robotics, Vol. 2. - Berlin, Heidelberg : Springer. - 9783642256455 - 9783642256462 ; , s. 79-86
-
Book chapter (other academic/artistic)abstract
- Small-size soccer league is a division of the RoboCup (Robot world cup) competitions. Each team uses its own designed hardware and software to compete with othersunder defined rules. There are two kinds of data which the strategy system will receive from the dedicated server, one of them is the referee commands, and the other one is vision data. However, due to the network delay and the vision noise, we have to process the data before we can actually use it. Therefore, a certain mechanism is needed in this case.Instead of using some prevalent and complex algorithms, this paper proposes to solve this problem from simple kinematics and mathematics point of view, which can be implemented effectively by hobbyists and undergraduate students. We divide this problem by the speed status and deal it in three different situations. Testing results show good performance with this algorithm and great potential in filtering vision data thus forecasting actual coordinates of tracking objects.
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| 46. |
- Somanath, Sanjay, 1994, et al.
(author)
-
Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
- 2024
-
In: Remote Sensing. - 2072-4292. ; 16:11
-
Journal article (peer-reviewed)abstract
- A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field.
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| 47. |
- Ge, Chenjie, 1991, et al.
(author)
-
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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| 48. |
- ur Réhman, Shafiq, 1978-
(author)
-
Expressing emotions through vibration for perception and control
- 2010
-
Doctoral thesis (other academic/artistic)abstract
- This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facial expressions to characterize dynamic emotions. The basic emotional expressions with increasing intensity become curves on the manifold extended from the center. The blends of emotions lie between those curves, which could be defined analytically by the positions of the main curves. The manifold is the “Braille Code” of emotions. The developed methodology and technology has been extended for building assistive wheelchair systems to aid a specific group of disabled people, cerebral palsy or stroke patients (i.e. lacking fine motor control skills), who don’t have ability to access and control the wheelchair with conventional means, such as joystick or chin stick. The solution is to extract the manifold of the head or the tongue gestures for controlling the wheelchair. The manifold is rendered by a 2D vibration array to provide user of the wheelchair with action information from gestures and system status information, which is very important in enhancing usability of such an assistive system. Current research work not only provides a foundation stone for vibrotactile rendering system based on object localization but also a concrete step to a new dimension of human-machine interaction.
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| 49. |
- Mohamed, Sherif, et al.
(author)
-
Towards Dynamic Monocular Visual Odometry Based on an Event Camera and IMU Sensor
- 2020
-
In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. - Cham : Springer. ; , s. 249-263
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Conference paper (peer-reviewed)abstract
- Visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) have gained a lot of attention in the field of autonomous robots due to the high amount of information per unit cost vision sensors can provide. One main problem in VO techniques is the high amount of data that a pixelated image has, affecting negatively the overall performance of such techniques. An event-based camera, as an alternative to a normal frame-based camera, is a prominent candidate to solve this problem by considering only pixel changes in consecutive events that can be observed with high time resolution. However, processing the event data that is captured by event-based cameras requires specific algorithms to extract and track features applicable for odometry. We propose a novel approach to process the data of an event-based camera and use it for odometry. It is a hybrid method that combines the abilities of event-based and frame-based cameras to reach a near-optimal solution for VO. Our approach can be split into two main contributions that are (1) using information theory and non-euclidean geometry to estimate the number of events that should be processed for efficient odometry and (2) using a normal pixelated frame to determine the location of features in an event-based camera. The obtained experimental results show that our proposed technique can significantly increase performance while keeping the accuracy of pose estimation in an acceptable range.
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- Dobnik, Simon, 1977, et al.
(author)
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KILLE: a Framework for Situated Agents for Learning Language Through Interaction
- 2017
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In: Linköping Electronic Conference Proceedings. - Linköpings universitet : Linköping University Electronic Press. - 1650-3686 .- 1650-3740. - 9789176856017
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Conference paper (peer-reviewed)abstract
- We present KILLE, a framework for situated agents for learning language through interaction with its environment (perception) and with a human tutor (dialogue). We provide proof-of-concept evaluations of the usability of the system in two domains: learning of object categories and learning of spatial relations.
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