Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Joint Induction of Shape Features and Tree Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Order Structure, Correspondence, and Shape Based Categories
Shape, Contour and Grouping in Computer Vision
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Sketch-based image matching Using Angular partitioning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present the Multi Angular Descriptor (MAD), a new shape descriptor for shape based object recognition and image retrieval. In the binary case, the MAD descriptor captures the angular view to multi resolution rings from each contour point. Placing the rings in different heights enables capturing multi-level global/local features. In gray level, it captures the weighted distribution over relative positions of the shape points to multi resolution rings around the centroid. The multi angular descriptor is robust to noise and small deformations. Flexible parameters makes the MAD descriptor tunable to specific unique characteristics of the different tasks. The extension of the (MAD) descriptor to gray level shapes, can be seen as an extension of a shape context descriptor to be used with low quality gray level images avoiding poor results of the binarization process. Testing the proposed descriptor on the MNIST dataset [16] and a private dataset using two matching techniques gave better results comparing to the Shapes Context and the Histogram of Oriented Gradients (HOG) descriptors.