Abstract We introduce a new approach for part-based human pose estimation using multi-layer compo... more Abstract We introduce a new approach for part-based human pose estimation using multi-layer composite models, in which each layer is a tree-structured pictorial structure that models pose at a different scale and with a different graphical structure. At the highest level, the submodel acts as a person detector, while at the lowest level, the body is decomposed into a collection of many local parts. Edges between adjacent layers of the composite model encode cross-model constraints.
Abstract Attributes are visual concepts that can be detected by machines, understood by humans, a... more Abstract Attributes are visual concepts that can be detected by machines, understood by humans, and shared across categories. They are particularly useful for fine-grained domains where categories are closely related to one other (eg bird species recognition). In such scenarios, relevant attributes are often local (eg ���white belly���), but the question of how to choose these local attributes remains largely unexplored.
In this assignment, a simple system for image warping and stereo matching techniques is implement... more In this assignment, a simple system for image warping and stereo matching techniques is implemented. For the warping functions, the implementation provides rotation and projective transformation by using bilinear interpolation. For stereo matching, the implementation provides a simple block-based matching technique, a HMM technique using dynamic programming, a belief propagation technique using min convolution, and a graph cuts approach based on ��-�� swap move.
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Papers by Kun Duan