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IEEE Transactions on Pattern Analysis and Machine Intelligence
The algorithmic beauty of plants
The algorithmic beauty of plants
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
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International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Shape Descriptor Based on Circular Hidden Markov Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Representation and detection of shapes in images
Representation and detection of shapes in images
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking with Dynamic Hidden-State Shape Models
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Experiments with computer vision methods for hand detection
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
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This paper proposes a method for detecting shapes of variable structure in images with clutter. The term “variable structure” means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance.