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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. Image Pattern Recognition. Principal Investigator: Thamsanqa Moyo Supervisors: Dr Greg Foster and Professor Shaun Bangay.

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Image Pattern Recognition

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

  2. Presentation Outline Background (Why?) Objectives (What?) Approach to the Study (How?) Timeline (When?) Questions

  3. Background to the Study Hair identification in Zoology and Forensics Manual image reference systems

  4. Micrographs used in a Manual System D V D V V D Jackal Wildebeest (Gnu) Kudu

  5. Background to the Study • Recent Computer Image Identification Work • With cheetah and zebra

  6. Background to the Study • How this project fits in: • Builds on previous work • Computer-based vs manual process • Hair structure patterns vs skin patterns

  7. Objectives of the Study To investigate: Image pattern recognition techniques Apply the techniques in hair pattern identification Resulting in a system that will: Report probable identities of hair patterns in images Be accurate enough to supplement manual efforts

  8. Approach to the Study:The Process Patterns Feature Generation Feature Selection Classifier Design System Evaluation Sensor Image Manipulation Artificial Intelligence Figure Adapted from Theodoris et al (2003:6)

  9. Approach to the Study:Implementation Using ImageJ Image manipulation application Public domain application written in Java Plugins easily implemented in Java

  10. Stage1: Sensor Producing input for the process Image manipulation based stage Images sourced from Zoology Department

  11. Stage1: Sensor (Grayscale) (Binarized) (Grayscale) (Binarized) Impala Patterns Red Hartebeest Patterns

  12. Stage 2: Feature Generation Identify features from patterns Hair patterns are feature vectors Define feature representation Larger than necessary number created

  13. Stage 3:Feature Selection Selection of “best” features Considerations Computational complexity Capability of the classifier stage Produce training patterns (sets) Used in classifier design

  14. Stage 4:Classifier design Place patterns into appropriate classes Linear and Non-Linear Classifiers E.g. neural network and perceptron Artificial Intelligence based stage

  15. Stage 5: System Evaluation Assess Performance Use known and unknown patterns Compare with manual system Field trip

  16. Timeline Table 1: Extract of WBS found on Project Website • 238 days from 14 March • Iterative Process

  17. Conclusion Background (why?) Useful to disciplines such as Zoology and Forensics Objectives (what?) Hair Pattern Recognition System Approach (How?) 5 stage approach(Graphics to AI) Timeline (When?) 238 days from 14 March

  18. Questions • Background (Why?) • Objectives (What?) • Approach (How?) • Timeline (When?)

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