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Author : David A. Clausi, Yongping Zhao Source : Computers & Geosciences 29 (2003) 837-850

Grey level co-occurrence integrated algoritm (GLCIA) : a superior computational method to rapid determine co-occurrence probability texture features. Author : David A. Clausi, Yongping Zhao Source : Computers & Geosciences 29 (2003) 837-850 Speaker : Kai-Hung Chen Date : Dec. 8, 2004.

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Author : David A. Clausi, Yongping Zhao Source : Computers & Geosciences 29 (2003) 837-850

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  1. Grey level co-occurrence integrated algoritm (GLCIA) : a superior computational method to rapid determineco-occurrence probability texture features Author : David A. Clausi, Yongping Zhao Source : Computers & Geosciences 29 (2003) 837-850 Speaker : Kai-Hung Chen Date : Dec. 8, 2004

  2. Outline • Introduction • Method • Experiments • Conclusions

  3. Introduction • GLCM • GLCIA • GLCHS • GLCHH • GLCHSH • GLCHDH • Statics for co-occurrence probability

  4. GLCM(Gray level co-occurrence matrix) G=4 (0-3) θ=0 and 180 D=1 20 possible co-occurrence pairs Ex:(3.2) appears 2 times in the matrix Probability:0.1

  5. Statics for co-occurrence probability

  6. Method GLCHS (Gray level co-occurrence hybrid structure)

  7. Method GLCHH(Gray level co-occurrence hybrid histogram) A:normalized sum histogram B:normailized difference histogram

  8. Method GLCHSH(Gray level co-occurrence hybrid sum histogram)

  9. Method GLCHDH(Gray level co-occurrence hybrid difference histogram)

  10. Experiment 1/5 Computational time:μs/sample 8 statics:DIS,CON,IDM,INV,COR,UNI,ENT,MAX 5 statics: CON,IDM,INV,DIS,COR 4 statics: CON,IDM,INV,DIS

  11. Experiment 2/5 8 statics 5 statics 4 statics

  12. Experiment 3/5

  13. Experiment 4/5

  14. Experiment 5/5 image size:1000*1000 θ=0,90,180 and 270 Processor:2.0 GHz

  15. Conclusions • Quickly calculate co-occurrence probability • Especially for large-scale remote-sensing image

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