Speaker: Yi-Chun Ke Adviser: Bo-Chi Lai Ku-Yaw Chang

1. Comparison of two gabor texture descriptor for texture classification紋理分類法中使用兩種賈柏紋理描述子進行比對 Speaker: Yi-Chun Ke Adviser: Bo-Chi Lai Ku-Yaw Chang

2. Outline • Introduction • Material and Method • Results • Conclusion

3. Introduction • Traditional Garbo texture description • two-dimensional Gabor function • m(x, y) = |gmn(x, y) ∗ i(x, y)| • μ：mean δ：standard deviation • s：scale k：orientation • Descriptors=2 × s × k + 2

4. Introduction • Rayleigh Garbo texture description • 1-D Gabor function • m(x) = |gmn(x) ∗ i(x)| • s：scale k：orientation • Descriptors=s × k + 2

5. Introduction • Back propagation neural network(BPNN)

6. Method • Traditional Gabor texture descriptor • S=4 scales • K=6 orientations • Descriptors=2 × s × k + 2=50 • Rayleigh model Gabor texture descriptor • S=4 scales • K=6 orientations • Descriptors= s × k + 2=26

7. Method • Back propagation neural network(BPNN) • input nodes number:50 or 26 • output nodes number: 4 • hidden nodes: 10

8. Material Data 2 Data 1 Data 3

9. Results dataset 1 traditional Gabor descriptor dataset 1 Rayleigh model Gabor descriptor

10. Results dataset 2 traditional Gabor descriptor dataset 2 Rayleigh model Gabor descriptor

11. Results dataset 3 traditional Gabor descriptor dataset 3 Rayleigh model Gabor descriptor

12. Results

13. Conclusion

14. References • James A. Freeman and David M. Skapura, Netuoral Networks Algorithms,Applications,and Programming Techniques,1991,90-93 • SitaramBhagavathy, Jelena Te si c, and B. S. Manjunath, On the Rayleigh Nature of Gabor Filter Outputs, Digital Object Identifier 10.1109/ICIP Volume 3,2005, I11 – 745- I11 – 748 • Xu Zhan, Xingbo Sun, Lei Yuerong, Comparison of two gabor texture descriptor for texture classification , Information Engineering, 2009. ICIE '09. WASE International Conference on Volume 1, 2009, 52 – 56 • Technology Exponent • http://www.tek271.com/?about=docs/neuralNet/IntoToNeuralNets.html