Morphological structuring element decomposition
Computer Vision, Graphics, and Image Processing
Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Shape Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of Convex Polygonal Morphological Structuring Elements into Neighborhood Subsets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of Arbitrarily Shaped Morphological Structuring Elements
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Note on Park and Chin's Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Efficient morphological shape representation
IEEE Transactions on Image Processing
Morphological decomposition of 2-D binary shapes into convex polygons: a heuristic algorithm
IEEE Transactions on Image Processing
Efficient morphological shape representation with overlapping disk components
IEEE Transactions on Image Processing
Journal of Visual Communication and Image Representation
Shape recognition and retrieval: A structural approach using velocity function
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Shape matching using coarse descriptors
International Journal of Computational Vision and Robotics
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This paper presents a novel shape representation algorithm based on mathematical morphology. It consists of two steps. Firstly, an input shape is decomposed into a union of meaningful convex subparts by a recursive scheme. Each subpart is obtained by repeatedly applying condition expansion to a seed, which is selected by utilizing the skeleton information. Secondly, the shape of each subpart is approximated by a morphological dilation of basic structuring elements. The location and direction of the subpart are represented respectively by two parameters. Thus the given shape is represented by a union set of a number of three-dimensional vectors. Experiments show that the new algorithm is immune to noise and occlusion, and invariant under rotation, translation and scaling. Compared to other algorithms, it achieves more natural looking shape components and more concise representation at lower computation costs and coding costs.