Study of Thinning Algorithms.

Date of Submission

December 1993

Date of Award

Winter 12-12-1994

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Pal, Srimanta (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

Thinning is of the important low level segmentation one procedure. There are number of thinning algorithms, but no one is good. Existing generalised in suitable to a particular class of images but not. thinning algorithms (chapter 1> are not. the sense that particular method may be to all knd of images. There doesnt exist any generalised model for measuring time complexity of thinning algorithms. As their performance a result be compared in order to select with can minimum time complexity with better skeletone. Most of the thinning algorithms are iterative approximation method, when in most of the cases shape of the skeletone are not preserve by the approxdmation method. As a result inaccurate results re submitted to the next stage thus error propagation has occurred. One model of analysis of the most widely + ed template matching thinning aigorithms, based on mark ov process method Cchapter 3) for conducting average case analysis of thinning algorithms in order to measure their performance, have been proposed. Also we have designed a model for the generation of random binary image (chapter 2) which are either normally distributed or 4/8 connected uniformlly distributed. We have used these binary images study the emperical to average performance of some template matching thinning algorithms.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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