Characteristic Pattern Based on Mathematical Morphology
Shape analysis is a very important issue in image analysis and computer vision. This paper describes a methodology using morphological operations to classify shapes by their decomposed components according to morphological structuring elements. A method called characteristic pattern is introduced to extract unique representations of an object shape among other shapes. Shape size analysis using mathematical morphology was introduced by Serra  where size criteria are discussed and geometrical properties of morphological processing on shapes are presented with morphological measurement. With size criteria, local area size parameters and global shape size distribution are both counted in Lebesque measure. Recent development of shape distribution can be found in [3,4,5,9]. In , pattern spectrum is used to describe the size distribution. Shape decomposition is another approach toward shape analysis, similar to a morphological skeletonization process. In , decomposition is completed by using a simplest object component (a disk) and analysis of an image is through a union of disks. The characteristic pattern concept is introduced in Section 2 of this paper to provide another approach toward shape analysis and matching. The basic concept is to derive a specific pattern associated with the object shape while other shapes within sample space do not possess this pattern orientation. We call this pattern orientation characteristic pattern. Characteristic pattern can be used to identify objects and classify shapes through decomposition — a parallel process using only local neighborhood pixel information.
KeywordsCharacteristic Pattern Sample Space Pattern Anal Shape Analysis Object Shape
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- G. Matheron, Random Set and Integral Geometry, Wiley, New York, 1975.Google Scholar
- J. Serra, Ed., Image Analysis and Mathematical Morphology, Vol. 2. Academic Press, New York, 1988.Google Scholar
- C. R. Giardina and E. R. Dougherty, Morphological Methods in Image and Signal Processing, Prentice, Englewood Cliffs, NJ, 1987.Google Scholar
- F. Leymarie and M. D. Levine, Curvature morphology, Technical Report TRCIM-88-26, McGill Research Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada.Google Scholar
- Z. Zhou and A. N. Venetsanopoulos, Generic ribbons: a morphological approach towards natural natural shape decomposition. In Visual Communications and Image Processing IV (1989), pp. 170-180, Phil. PA, Nov. 1989. SPIE-1199, SPIE-1199.Google Scholar
- E. R. Dougherty and C. R. Giardina, Closed-form representation of convolution, dilation, and erosion in the context of image algebra. In Proc. Computer Vision and Pattern Recognition 88, pp. 754–759, Ann Arbor, MI, June 1988.Google Scholar