| 1. |
- Jönsson, Daniel
(författare)
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Enhancing Salient Features in Volumetric Data Using Illumination and Transfer Functions
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The visualization of volume data is a fundamental component in the medical domain. Volume data is used in the clinical work-flow to diagnose patients and is therefore of uttermost importance. The amount of data is rapidly increasing as sensors, such as computed tomography scanners, become capable of measuring more details and gathering more data over time. Unfortunately, the increasing amount of data makes it computationally challenging to interactively apply high quality methods to increase shape and depth perception. Furthermore, methods for exploring volume data has mostly been designed for experts, which prohibits novice users from exploring volume data. This thesis aims to address these challenges by introducing efficient methods for enhancing salient features through high quality illumination as well as methods for intuitive volume data exploration.Humans are interpreting the world around them by observing how light interacts with objects. Shadows enable us to better determine distances while shifts in color enable us to better distinguish objects and identify their shape. These concepts are also applicable to computer generated content. The perception in volume data visualization can therefore be improved by simulating real-world light interaction. This thesis presents efficient methods that are capable of interactively simulating realistic light propagation in volume data. In particular, this work shows how a multi-resolution grid can be used to encode the attenuation of light from all directions using spherical harmonics and thereby enable advanced interactive dynamic light configurations. Two methods are also presented that allow photon mapping calculations to be focused on visually changing areas.The results demonstrate that photon mapping can be used in interactive volume visualization for both static and time-varying volume data.Efficient and intuitive exploration of volume data requires methods that are easy to use and reflect the objects that were measured. A value that has been collected by a sensor commonly represents the material existing within a small neighborhood around a location. Recreating the original materials is difficult since the value represents a mixture of them. This is referred to as the partial-volume problem. A method is presented that derives knowledge from the user in order to reconstruct the original materials in a way which is more in line with what the user would expect. Sharp boundaries are visualized where the certainty is high while uncertain areas are visualized with fuzzy boundaries. The volume exploration process of mapping data values to optical properties through the transfer function has traditionally been complex and performed by expert users. A study at a science center showed that visitors favor the presented dynamic gallery method compared to the most commonly used transfer function editor.
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| 2. |
- Kucher, Kostiantyn, et al.
(författare)
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Text Visualization Techniques : Taxonomy, Visual Survey, and Community Insights
- 2015
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Ingår i: Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15). - : IEEE. - 9781467368797 ; , s. 117-121
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Konferensbidrag (refereegranskat)abstract
- Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data.
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| 3. |
- Lundin Palmerius, Karljohan, 1977-, et al.
(författare)
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Visualizing and Exploring Heat in a Science Center
- 2022
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Ingår i: Thermal Cameras in Science Education. - Cham, Schweiz : Springer. - 9783030852870 - 9783030852900 - 9783030852887 ; , s. 187-203
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Recent research shows that infrared cameras can help students visualize and interpret notoriously challenging thermal concepts. This chapter describes the application of thermal visualization in a public setting. Specifically, we present the design and implementation of an augmented reality system for the real-time projection of thermal imagery onto objects. Examples of hands-on activities for visualizing thermal processes with the system include conduction and insulation, rubber band thermodynamics, friction, impact heating, enthalpy of chemical reactions, radiation wavelength, mixing liquids, and heat of evaporation. We discuss how the interactive activities might provide pedagogical opportunities for accessing and engaging with thermal phenomena in a science center context. Practical considerations of the system for public exhibition spaces are also given attention.
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| 4. |
- Daoud, Adel, 1981, et al.
(författare)
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Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
- 2023
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Ingår i: Social Indicators Research. - : SPRINGER. - 0303-8300 .- 1573-0921. ; 167:1-3, s. 475-505
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Granlund, Gösta H.
(författare)
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A Nonlinear, Image-content Dependent Measure of Image Quality
- 1977
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, considerable research effort has been devoted to the development of useful descriptors for image quality. The attempts have been hampered by i n complete understanding of the operation of the human visual system. This has made it difficult to relate physical measures and perceptual traits.A new model for determination of image quality is proposed. Its main feature is that it tries to invoke image content into consideration. The model builds upon a theory of image linearization, which means that the information in an image can well enough be represented using linear segments or structures within local spatial regions and frequency ranges. This implies a l so a suggestion that information in an image has to do with one- dimensional correlations. This gives a possibility to separate image content from noise in images, and measure them both.Also a hypothesis is proposed that the visual system of humans does in fact perform such a linearization.
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| 6. |
- Le, Minh Ha, et al.
(författare)
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AnonFACES: Anonymizing Faces Adjusted to Constraints on Efficacy and Security
- 2020
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Ingår i: WPES 2020 - Proceedings of the 19th Workshop on Privacy in the Electronic Society. - New York, NY, USA : ACM. ; , s. 87-100, s. 87-100
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Konferensbidrag (refereegranskat)abstract
- Image data analysis techniques such as facial recognition can threaten individuals’ privacy. Whereas privacy risks often can be reduced by adding noise to the data, this approach reduces the utility of the images. For this reason, image de-identification techniques typically replace directly identifying features (e.g., faces, car number plates) present in the data with synthesized features, while still preserving other non-identifying features. As of today, existing techniques mostly focus on improving the naturalness of the generated synthesized images, without quantifying their impact on privacy. In this paper, we propose the first methodology and system design to quantify, improve, and tune the privacy-utility trade-off, while simultaneously also improving the naturalness of the generated images. The system design is broken down into three components that address separate but complementing challenges. This includes a two-step cluster analysis component to extract low-dimensional feature vectors representing the images (embedding) and to cluster the images into fixed-sized clusters. While the importance of good clustering mostly has been neglected in previous work, we find that our novel approach of using low-dimensional feature vectors can improve the privacy-utility trade-off by better clustering similar images. The use of these embeddings has been found particularly useful when wanting to ensure high naturalness and utility of the synthetically generated images. By combining improved clustering and incorporating StyleGAN, a state-of-the-art Generative Neural Network, into our solution, we produce more realistic synthesized faces than prior works, while also better preserving properties such as age, gender, skin tone, or even emotional expressions. Finally, our iterative tuning method exploits non-linear relations between privacy and utility to identify good privacy-utility trade-offs. We note that an example benefit of these improvements is that our solution allows car manufacturers to train their autonomous vehicles while complying with privacy laws.
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| 7. |
- Liu, Yang, et al.
(författare)
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Movement Status Based Vision Filter for RoboCup Small-Size League
- 2012
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Ingår i: Advances in Automation and Robotics, Vol. 2. - Berlin, Heidelberg : Springer. - 9783642256455 - 9783642256462 ; , s. 79-86
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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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| 8. |
- Mårtensson, T., et al.
(författare)
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Efficient and effective exploratory testing of large-scale software systems
- 2021
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 174
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Tidskriftsartikel (refereegranskat)abstract
- Context: Exploratory testing plays an important role in the continuous integration and delivery pipelines of large-scale software systems, but a holistic and structured approach is needed to realize efficient and effective exploratory testing. Objective: This paper seeks to address the need for a structured and reliable approach by providing a tangible model, supporting practitioners in the industry to optimize exploratory testing in each individual case. Method: The reported study includes interviews, group interviews and workshops with representatives from six companies, all multi-national organizations with more than 2,000 employees. Results: The ExET model (Excellence in Exploratory Testing) is presented. It is shown that the ExET model allows companies to identify and visualize strengths and improvement areas. The model is based on a set of key factors that have been shown to enable efficient and effective exploratory testing of large-scale software systems, grouped into four themes: “The testers’ knowledge, experience and personality”, “Purpose and scope”, “Ways of working” and “Recording and reporting”. Conclusions: The validation of the ExET model showed that the model is novel, actionable and useful in practice, showing companies what they should prioritize in order to enable efficient and effective exploratory testing in their organization.
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| 9. |
- Bleser, Gabriele, et al.
(författare)
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Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks
- 2015
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user’s pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.
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| 10. |
- Sundvall, Erik, 1973-
(författare)
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Scalability and Semantic Sustainability in Electronic Health Record Systems
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This work is a small contribution to the greater goal of making software systems used in healthcare more useful and sustainable. To come closer to that goal, health record data will need to be more computable and easier to exchange between systems.Interoperability refers to getting systems to work together and semantics concerns the study of meanings. If Semantic interoperability is achieved then information entered in one information system is usable in other systems and reusable for many purposes. Scalability refers to the extent to which a system can gracefully grow by adding more resources. Sustainability refers more to how to best use available limited resources. Both aspects are important.The main focus and aim of the thesis is to increase knowledge about how to support scalability and semantic sustainability. It reports explorations of how to apply aspects of the above to Electronic Health Record (EHR) systems, associated infrastructure, data structures, terminology systems, user interfaces and their mutual boundaries.Using terminology systems is one way to improve computability and comparability of data. Modern complex ontologies and terminology systems can contain hundreds of thousands of concepts that can have many kinds of relationships to multiple other concepts. This makes visualization challenging. Many visualization approaches designed to show the local neighbourhood of a single concept node do not scale well to larger sets of nodes. The interactive TermViz approach described in this thesis, is designed to aid users to navigate and comprehend the context of several nodes simultaneously. Two applications are presented where TermViz aids management of the boundary between EHR data structures and the terminology system SNOMED CT.The amount of available time from people skilled in health informatics is limited. Adequate methods and tools are required to develop, maintain and reuse health-IT solutions in a sustainable way. Multiple levels of modelling including a fixed reference model and another layer of flexible reusable ‘archetypes’ for domain specific data structures, is an approach with that aim used in openEHR and the ISO 13606 standard. This approach, including learning, implementing and managing it, is explored from different angles in this thesis. An architecture applying Representational State Transfer (REST) to archetype-based EHR systems, in order to address scalability, is presented. Combined with archetyping this architecture also aims at enabling a sustainable way of continuously evolving multi-vendor EHR solutions. An experimental open source implementation of it, aimed for learning and prototyping, is also presented.Manually changing database structures used for storage every time new versions of archetypes and associated data structures are needed is likely not a sustainable activity. Thus storage systems that can handle change with minimal manual interventions are desirable. Initial explorations of performance and scalability in such systems are also reportedGraphical user interfaces focused on EHR navigation, time-perspectives and highlighting of EHR content are also presented – illustrating what can be done with computable health record data and the presented approaches.Desirable aspects of semantic sustainability have been discussed, including: sustainable use of limited resources (such as available time of skilled people), and reduction of unnecessary risks. A semantic sustainability perspective should be inspired and informed by research in complex systems theory, and should also include striving to be highly aware of when and where technical debt is being built up. Semantic sustainability is a shared responsibility.The combined results presented contribute to increasing knowledge about ways to support scalability and semantic sustainability in the context of electronic health record systems. Supporting tools, architectures and approaches are additional contributions.
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