| 1. |
- Afkham, Heydar Maboudi, et al.
(författare)
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A topological framework for training latent variable models
- 2014
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 9781479952083 ; , s. 2471-2476
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Konferensbidrag (refereegranskat)abstract
- We discuss the properties of a class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization. In this paper, we focus on the learning of such models by introducing a topological framework and show how it is possible to both reduce the learning complexity and produce more robust decision boundaries. We will also argue how our framework can be used for producing robust decision boundaries without exploiting the dataset bias or relying on accurate annotations. To experimentally evaluate our method and compare with previously published frameworks, we focus on the problem of image classification with object localization. In this problem, the correct location of the objects is unknown, during both training and testing stages, and is considered as a latent variable.
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| 2. |
- Afkham, Heydar Maboudi, et al.
(författare)
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Gradual improvement of image descriptor quality
- 2014
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Ingår i: ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and and Technology Publications. - 9789897580185 ; , s. 233-238
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a framework for gradually improving the quality of an already existing image descriptor. The descriptor used in this paper (Afkham et al., 2013) uses the response of a series of discriminative components for summarizing each image. As we will show, this descriptor has an ideal form in which all categories become linearly separable. While, reaching this form is not feasible, we will argue how by replacing a small fraction of these components, it is possible to obtain a descriptor which is, on average, closer to this ideal form. To do so, we initially identify which components do not contribute to the quality of the descriptor and replace them with more robust components. Here, a joint feature selection method is used to find improved components. As our experiments show, this change directly reflects in the capability of the resulting descriptor in discriminating between different categories.
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| 3. |
- Afkham, Heydar Maboudi, et al.
(författare)
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Initialization framework for latent variable models
- 2014
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Ingår i: ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and and Technology Publications. - 9789897580185 ; , s. 227-232
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Konferensbidrag (refereegranskat)abstract
- In this paper, we discuss the properties of a class of latent variable models that assumes each labeled sample is associated with set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good example of such models. While Latent SVM framework (LSVM) has proven to be an efficient tool for solving these models, we will argue that the solution found by this tool is very sensitive to the initialization. To decrease this dependency, we propose a novel clustering procedure, for these problems, to find cluster centers that are shared by several sample sets while ignoring the rest of the cluster centers. As we will show, these cluster centers will provide a robust initialization for the LSVM framework.
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| 4. |
- Baisero, Andrea, et al.
(författare)
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The path kernel : A novel kernel for sequential data
- 2015
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Ingår i: Pattern Recognition. - Cham : Springer Berlin/Heidelberg. - 9783319126098 ; , s. 71-84
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Konferensbidrag (refereegranskat)abstract
- We define a novel kernel function for finite sequences of arbitrary length which we call the path kernel. We evaluate this kernel in a classification scenario using synthetic data sequences and show that our kernel can outperform state of the art sequential similarity measures. Furthermore, we find that, in our experiments, a clustering of data based on the path kernel results in much improved interpretability of such clusters compared to alternative approaches such as dynamic time warping or the global alignment kernel.
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| 5. |
- Baisero, Andrea, et al.
(författare)
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The Path Kernel
- 2013
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Ingår i: ICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. - 9789898565419 ; , s. 50-57
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Konferensbidrag (refereegranskat)abstract
- Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data. In this paper, we present a new kernel for encoding sequential data. We present our results comparing the proposed kernel to the state of the art, showing a significant improvement in classification and a much improved robustness and interpretability.
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| 6. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Probabilistic Consolidation of Grasp Experience
- 2016
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. ; , s. 193-200
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Konferensbidrag (refereegranskat)abstract
- We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.
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| 7. |
- Bergström, Niklas, 1978-, et al.
(författare)
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On-line learning of temporal state models for flexible objects
- 2012
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Ingår i: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids). - : IEEE. - 9781467313698 ; , s. 712-718
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Konferensbidrag (refereegranskat)abstract
- State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model before-hand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task and execution. For example, when folding a cloth, the representation will mainly depend on the way the folding is executed.
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| 8. |
- Bergström, Niklas, 1978-, et al.
(författare)
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Scene Understanding through Autonomous Interactive Perception
- 2011
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Ingår i: Computer Vision Systems. - Berlin, Heidelberg : Springer Verlag. - 9783642239670 - 3642239676 ; , s. 153-162
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Konferensbidrag (refereegranskat)abstract
- We propose a framework for detecting, extracting and mod-eling objects in natural scenes from multi-modal data. Our frameworkis iterative, exploiting different hypotheses in a complementary manner.We employ the framework in realistic scenarios, based on visual appear-ance and depth information. Using a robotic manipulator that interactswith the scene, object hypotheses generated using appearance informa-tion are confirmed through pushing. The framework is iterative, eachgenerated hypothesis is feeding into the subsequent one, continuously re-fining the predictions about the scene. We show results that demonstratethe synergic effect of applying multiple hypotheses for real-world sceneunderstanding. The method is efficient and performs in real-time.
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| 9. |
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| 10. |
- Caccamo, Sergio, et al.
(författare)
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Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
- 2016
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2153-0858. - 9781509037629 ; , s. 582-589
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Konferensbidrag (refereegranskat)abstract
- In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demostrate how to use the online framework for object detection and terrain classification.
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