Probabilistic Methods for Finding People
International Journal of Computer Vision
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-stage Contour Based Detection of Deformable Objects
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching via sequential monte carlo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Grouping active contour fragments for object recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts structure to different object classes as long as they have similar shape. The spatial layout between parts is described by a non-parametric density, which is more flexible and easier to learn than commonly used Gaussian or other parametric distributions. We express object detection as state estimation inference executed using a novel Particle Filters (PF) framework with static observations, which is quite different from previous PF methods. Although the underlying graph structure of our model is given by a fully connected graph, the proposed PF algorithm efficiently linearizes it by exploring the conditional dependencies of the nodes representing contour parts. Experimental results demonstrate that the proposed approach can not only yield very good detection results but also accurately locates contours of target objects in cluttered images.