1. Image Pattern Recognition The identification of animal species through the classification of hair patterns using image pattern recognition: A case study of identifying cheetah prey. Principal Investigator: Thamsanqa Moyo Supervisors: Dr Greg Foster and Professor Shaun Bangay.

2. Presentation Outline Problem Statement Objectives Approach Research Done Conclusion

3. Problem Statement Hair identification in Zoology and Forensics Subjectivity

4. Problem Statement First application of automated image pattern recognition techniques to the problem of classifying African mammalian species using hair patterns. – based on the numerical and statistical analysis of hair patterns.

5. Approach to the Study: Lack of literature focused on hair recognition Multi-disciplinary nature New process designed

6. Approach to the Study: Process Stages Sensor Feature Generation Feature Selection Classifier Design System Evaluation Figure Adapted from Theodoris et al (2003:6) Image Capture Each stage detailed next

7. Research Done: How can hair pattern images be captured? –Based in Zoology Department –2 approaches considered Image Capture SEM Light Microscope

8. Research Done: Image Capture SEMLight Microscope Scale Patterns Cross Section Patterns

9. Scale Patterns –Use SEM –Better representation of texture in image Research Done: Image Capture SEM Light Microscope

10. Cross section patterns –Use Light microscope –2D shape preferred to a 3D shape Research Done: Image Capture SEM Light Microscope

11. Decisions affecting design –Scale patterns texture based –Cross section patterns shape based –2 separate sub-processes –Decision not to combine their results Research Done: Image Capture

12. Research Done: Sensor What image manipulation techniques are applied in a hair pattern recognition process? –Scale Pattern Processing User defined ROI Handle RST variations No need to cater for reflection variations Convert to greyscale

13. Research Done: Sensor Stage What image manipulation techniques are applied in a hair pattern recognition process? –Cross section pattern processing User defined ROI Image segmentation and thresholding Challenges

14. Research Done: Sensor Stage OriginalThresholding Edge DetectionGrab Cut + Thresholding

15. Findings –Most sensitive stage of the process –Cross section patterns best extracted with Grab Cut Contributions –First test of Grab Cut technique in a real world problem Research Done: Sensor Stage

16. Research Done: Feature Extraction How can features be extracted? Scale Pattern Processing –Gabor filters –Capture pattern orientation and frequency information –Produces n number of filtered images where n is the size of the Gabor filter-bank

17. Research Done: Feature Extraction Filtered Images from a Gabor Filter of size 4. Images filtered at initial orientation of 0 degrees Images filtered at initial orientation of 180 degrees

18. Research Done: Feature Extraction How can features be extracted? Cross Section Processing –Hu’s 7 moments –RST invariant shape descriptors –Calculated from central moments –Require black and white image

19. Research Done: Feature Selection What selection of features is necessary Scale Pattern Processing –Image tessellation –Use of variance or average absolute deviation

20. Research Done: Feature Selection What selection of features is necessary? Cross section processing –None required for Hu’s moments –Would affect scalability of the process

21. Research Done: Classifier Design What mechanisms can be used to classify features? –Scale Pattern Processing Euclidean distance measure 3 Scale patterns used to train –Cross Section Processing Euclidean distance measure or Hamming distance measure 10 cross section patterns used to train

22. Research Done: Results From implementation using: –ImageJ plugins written in Java 1.4 –25 scale patterns processed –50 cross section patterns processed

23. Research Done: Results Scale pattern results (Variance)

24. Research Done: Results Scale pattern results (AAD)

25. Research Done: Results Summary of scale pattern results: –AAD is a better feature selection method –Results most stable with 8 filters using AAD as feature selector –Explanation of this result

26. Research Done: Results Cross section pattern results

27. Research Done: Results Summary of cross section pattern results: –Euclidean distance overall classification rate: 26% –Hamming distance overall classification rate: 40% –Explanation of this result

28. Conclusion Findings and Contributions –Gabor filters and moments shown to provide hair pattern classification information –AAD performs better feature selection than variance – Hamming distance more suitable classifier of moments than Euclidean distance –First application of hair pattern recognition on African mammalian species hair.

29. Questions Manual Preparation Work Sensor Feature extraction Feature Selection Classifier Design Results