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Geometric Disentanglement for Generative Latent Shape Models
[article]
2019
arXiv
pre-print
Preserved Fulltext
Sven Dickinson, Allan Jepson, and Stavros Tsogkas contributed in their capacity as Professors and Postdoc at the University of Toronto, respectively. ...
arXiv:1908.06386v1
fatcat:dtjbjhjxz5dlnjkth5f3o4tkaa
DeepFlux for Skeletons in the Wild
[article]
2018
arXiv
pre-print
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Tsogkas and Kokkinos [45] extract hand-designed features at each pixel and train a classifier for symmetry detection. ...
arXiv:1811.12608v1
fatcat:pahjsjqtfbhzbmaawn4yd3ktxq
AMAT: Medial Axis Transform for Natural Images
[article]
2017
arXiv
pre-print
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Our code and annotations are available at https://github.com/tsogkas/amat . ...
Tsogkas and Kokkinos use multiple instance learning (MIL) to deal with the unknown scale and orientation during training [49] , while Shen et al. adapt a CNN with side outputs [53] for object skeleton ...
arXiv:1703.08628v2
fatcat:lnzslfhxsbbupgq3oekshmxwai
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
[article]
2021
arXiv
pre-print
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1 University of Toronto 2 Samsung Toronto AI Research Center 3 Vector Institute for Artificial Intelligence Disclaimer: Tristan Aumentado-Armstrong and Stavros Tsogkas contributed to this article in their ...
arXiv:2103.00142v1
fatcat:oefxuozl5bewtcnbvqoe66ccze
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels
[article]
2021
arXiv
pre-print
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Tool use requires reasoning about the fit between an object's affordances and the demands of a task. Visual affordance learning can benefit from goal-directed interaction experience, but current techniques rely on human labels or expert demonstrations to generate this data. In this paper, we describe a method that grounds affordances in physical interactions instead, thus removing the need for human labels or expert policies. We use an efficient sampling-based method to generate successful
arXiv:2106.14973v1
fatcat:g4jzc6pnzrcclmfzwpimtuja5a
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... ctories that provide contact data, which are then used to reveal affordance representations. Our framework, GIFT, operates in two phases: first, we discover visual affordances from goal-directed interaction with a set of procedurally generated tools; second, we train a model to predict new instances of the discovered affordances on novel tools in a self-supervised fashion. In our experiments, we show that GIFT can leverage a sparse keypoint representation to predict grasp and interaction points to accommodate multiple tasks, such as hooking, reaching, and hammering. GIFT outperforms baselines on all tasks and matches a human oracle on two of three tasks using novel tools.
Learning-Based Symmetry Detection in Natural Images
[chapter]
2012
Lecture Notes in Computer Science
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In this work we propose a learning-based approach to symmetry detection in natural images. We focus on ribbon-like structures, i.e. contours marking local and approximate reflection symmetry and make three contributions to improve their detection. First, we create and make publicly available a ground-truth dataset for this task by building on the Berkeley Segmentation Dataset. Second, we extract features representing multiple complementary cues, such as grayscale structure, color, texture, and
doi:10.1007/978-3-642-33786-4_4
fatcat:ts3ommyzq5dfpjxc6mrprjtffy
more »
... pectral clustering information. Third, we use supervised learning to learn how to combine these cues, and employ MIL to accommodate the unknown scale and orientation of the symmetric structures. We systematically evaluate the performance contribution of each individual component in our pipeline, and demonstrate that overall we consistently improve upon results obtained using existing alternatives.
Cycle-Consistent Generative Rendering for 2D-3D Modality Translation
[article]
2020
arXiv
pre-print
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For humans, visual understanding is inherently generative: given a 3D shape, we can postulate how it would look in the world; given a 2D image, we can infer the 3D structure that likely gave rise to it. We can thus translate between the 2D visual and 3D structural modalities of a given object. In the context of computer vision, this corresponds to a learnable module that serves two purposes: (i) generate a realistic rendering of a 3D object (shape-to-image translation) and (ii) infer a
arXiv:2011.08026v1
fatcat:irpf4btl6vdvvldr6zgqgfwxpi
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... 3D shape from an image (image-to-shape translation). In this paper, we learn such a module while being conscious of the difficulties in obtaining large paired 2D-3D datasets. By leveraging generative domain translation methods, we are able to define a learning algorithm that requires only weak supervision, with unpaired data. The resulting model is not only able to perform 3D shape, pose, and texture inference from 2D images, but can also generate novel textured 3D shapes and renders, similar to a graphics pipeline. More specifically, our method (i) infers an explicit 3D mesh representation, (ii) utilizes example shapes to regularize inference, (iii) requires only an image mask (no keypoints or camera extrinsics), and (iv) has generative capabilities. While prior work explores subsets of these properties, their combination is novel. We demonstrate the utility of our learned representation, as well as its performance on image generation and unpaired 3D shape inference tasks.
Sub-cortical brain structure segmentation using F-CNN's
[article]
2016
arXiv
pre-print
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Code, computed probability maps, and more results can be found at https://github.com/tsogkas/ brainseg. ...
arXiv:1602.02130v1
fatcat:xsc2nhefmzfzbn5jgmul3km4wa
Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
[article]
2020
arXiv
pre-print
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A completely different, unsupervised approach, the AMAT, was proposed by Tsogkas and Dickinson [36] . ...
Departing from this trend, Tsogkas and Dickinson defined the first complete MAT for color images, formulating medial axis extraction as a set cover problem [36] . ...
arXiv:2004.02677v1
fatcat:vohghovsererffnewmmzklfkqa
Prior-Based Coregistration and Cosegmentation
[chapter]
2016
Lecture Notes in Computer Science
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We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric.
doi:10.1007/978-3-319-46723-8_61
fatcat:ommhkeaafjhe7opwagh2fsay3u
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... esults on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.
Segmentation-Aware Deformable Part Models
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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In this work we propose a technique to combine bottomup segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs). The merit of our approach lies in 'cleaning up' the lowlevel HOG features by exploiting the spatial support of SLIC superpixels; this can be understood as using segmentation to split the feature variation into object-specific and background changes. Rather than committing to a single segmentation we use a large pool
doi:10.1109/cvpr.2014.29
dblp:conf/cvpr/TrullsTKSM14
fatcat:gefw6vemdfddjoqxeezu65mvra
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... SLIC superpixels and combine them in a scale-, position-and object-dependent manner to build soft segmentation masks. The segmentation masks can be computed fast enough to repeat this process over every candidate window, during training and detection, for both the root and part filters of DPMs. We use these masks to construct enhanced, backgroundinvariant features to train DPMs. We test our approach on the PASCAL VOC 2007, outperforming the standard DPM in 17 out of 20 classes, yielding an average increase of 1.7% AP. Additionally, we demonstrate the robustness of this approach, extending it to dense SIFT descriptors for large displacement optical flow.
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
[article]
2021
arXiv
pre-print
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. * Stavros Tsogkas and Sarah Parisot contributed to this article in their personal capacity as an Adjunct Professor at the University of Toronto and Visiting Scholar at Mila, respectively. ...
Tsogkas is with the University of Toronto and Samsung AI Research Center, Toronto. • E. ...
arXiv:2106.06440v2
fatcat:ncnobbj7mndbpdgmuzz6cefumq
Accurate Human-Limb Segmentation in RGB-D Images for Intelligent Mobility Assistance Robots
2015
2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
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Mobility impairment is one of the biggest challenges faced by elderly people in today's society. The inability to move about freely poses severe restrictions on their independence and general quality of life. This work is dedicated to developing intelligent robotic platforms that assist users to move without requiring a human attendant. This work was done in the context of an EU project involved in developing an intelligent robot for elderly user assistance. The robot is equipped with a Kinect
doi:10.1109/iccvw.2015.64
dblp:conf/iccvw/ChandraTK15
fatcat:5x63ha4nzbci5jw5diteyrslh4
more »
... ensor, and the visioncomponent of the project has the responsibility of locating the user, estimating the user's pose, and recognizing gestures by the user. All these goals can take advantage of a method that accurately segments human-limbs in the colour (RGB) and depth (D) images captured by the Kinect sensor. We exploit recent advances in deep-learning to develop a system that performs accurate semantic segmentation of human limbs using colour and depth images. Our novel technical contributions are the following: 1) we describe a scheme for manual annotation of videos, that eliminates the need to annotate segmentation masks in every single frame; 2) we extend a state of the art deep learning system for semantic segmentation, to exploit diverse RGB and depth data, in a single framework for training and testing; 3) we evaluate different variants of our system and demonstrate promising performance, as well the contribution of diverse data, on our in-house Human-Limb dataset. Our method is very efficient, running at 8 frames per second on a GPU.
2017 ICCV Challenge: Detecting Symmetry in the Wild
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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The bottom row image is from the BMAX500 dataset and the algorithm is from Tsogkas and Kokkinos [47] . symmetry or medial axis basis. ...
We also compare against Loy and Ek- lundh's algorithm [26] (LE), Tsogkas and Kokkinos's algorithm [47] (MIL), the Structured Random Forest method of Teo et al. ...
doi:10.1109/iccvw.2017.198
dblp:conf/iccvw/FunkLOTSCDL17
fatcat:jbj2fad3rrbcrl2spcdzn2gwl4
Sub-cortical brain structure segmentation using F-CNN'S
2016
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Preserved Fulltext
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The file type is application/pdf.
Code, computed probability maps, and more results can be found at https://github.com/tsogkas/ brainseg. ...
doi:10.1109/isbi.2016.7493261
dblp:conf/isbi/ShakenTFLKPK16
fatcat:pqgyiy6skbfunfrd34vuzqz5ly
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