Mass Displacement Networks

Despite the large improvements in performance attained by using deep learning in computer vision, one can often further improve results with some additional post-processing that exploits the geometric nature of the underlying task. This commonly involves displacing the posterior distribution of a CNN in a way that makes it more appropriate for the task at hand, e.g. better aligned with local image features, or more compact. In this work we integrate this geometric post-processing within a deep architecture, introducing a differentiable and probabilistically sound counterpart to the common geometric voting technique used for evidence accumulation in vision. We refer to the resulting neural models as Mass Displacement Networks (MDNs), and apply them to human pose estimation in two distinct setups: (a) landmark localization, where we collapse a distribution to a point, allowing for precise localization of body keypoints and (b) communication across body parts, where we transfer evidence from one part to the other, allowing for a globally consistent pose estimate. We evaluate on large-scale pose estimation benchmarks, such as MPII Human Pose and COCO datasets, and report systematic improvements when compared to strong baselines.Comment: 12 pages, 4 figure

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features

International audienceEstablishing visual correspondences under large intra-class variations requires analyzing images at different levels , from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by "hyper-pixels" that leverage a small number of relevant features selected among early to late layers of a convolutional neu-ral network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel flow, sets a new state of the art on three standard benchmarks as well as a new dataset, SPair-71k, which contains a significantly larger number of image pairs than existing datasets, with more accurate and richer annotations for in-depth analysis

Development of a Low-Cost Multi-Camera Star Tracker for Small Satellites

This paper presents a novel small satellite star tracker that uses multiple low-cost cameras to achieve viable attitude determination performance. The theoretical analysis of the star detectability improvement by stacking images from multiple cameras is presented. An image processing algorithm is developed to combine images from multiple cameras with various focal lengths, principal point offsets, distortions, and misalignments. The star tracker also implements other algorithms including the region growing algorithm, the intensity weighted centroid algorithm, the geometric voting algorithm for star identification, and the singular value decomposition algorithm for attitude determination. A star tracker software simulator is used to test the algorithms by generating star images with sensor noises, lens defocusing, and lens distortion. A hardware prototype is assembled, and preliminary night sky testing was conducted to verify the feasibility of the selected hardware. The flight hardware for the star tracker is being developed in the Laboratory for Advanced Space Systems at Illinois (LASSI) at the University of Illinois at Urbana Champaign for future CubeSat missions

Development of a low-cost multi-camera star tracker for small satellites

This thesis presents a novel small satellite star tracker that uses an array of low-cost, off the shelf imaging sensors to achieve high accuracy attitude determination performance. The theoretical analysis of improvements in star detectability achieved by stacking images from multiple cameras is presented. An image processing algorithm is developed to combine images from multiple cameras with arbitrary focal lengths, principal point offsets, distortions, and misalignments. The star tracker also implements other algorithms including the region growing algorithm, the intensity weighted centroid algorithm, the geometric voting algorithm for star identification, and the singular value decomposition algorithm for attitude determination. A star tracker software simulator is used to test the algorithms by generating star images with sensor noises, lens defocusing, and lens distortion. A hardware prototype is being assembled for eventual night sky testing to verify simulated performance levels. Star tracker flight hardware is being developed in the Laboratory for Advanced Space Systems at Illinois (LASSI) at the University of Illinois at Urbana Champaign for future CubeSat missions

SCNet: Learning Semantic Correspondence

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.Comment: ICCV 201

Voter Compatibility In Interval Societies

In an interval society, voters are represented by intervals on the real line, corresponding to their approval sets on a linear political spectrum. I imagine the society to be a representative democracy, and ask how to choose members of the society as representatives. Following work in mathematical psychology by Coombs and others, I develop a measure of the compatibility (political similarity) of two voters. I use this measure to determine the popularity of each voter as a candidate. I then establish local “agreeability” conditions and attempt to find a lower bound for the popularity of the best candidate. Other results about certain special societies are also obtaine

On removing the Condorcet influence from pairwise elections data

Recent developments in voting theory show that Condorcet profiles embedded in electorates are responsible for conflicts between pairwise voting methods and for reversals of rankings under positional methods whenever candidates are dropped or added. Because of the strong symmetry of the rankings of the candidates within these profiles, it can be argued that Condorcet profiles represent complete ties between the candidates so far as election outcomes are concerned. Hence removing their influence from pairwise tallies should not matter and moreover is justified because of the distortions they induce. The paper discusses a method of removing or reducing the influence of Condorcet profiles from pairwise elections data