1. Individual Zebra Identification
Hans Krijger
Supervised by Shaun Bangay and Greg Foster

2. The Problem Tracking individual zebra is useful
Feeding, breeding and migration trends
Social interaction
Wildlife management & ecological assessment
Traditional methods have drawbacks
Expense (Radio transmitters)
Time (Photographic methods)
Trauma during capture (Tranquillisation)

3. The Solution Computer aided matching Draw on existing knowledge Fast
Cost-effective
Non-intrusive
Draw on existing knowledge
Image processing methods
Fingerprint verification model
Machine vision pipeline

4. Solution Model Two main stages Important to standardise at each step
Reduce variation
Improve accuracy

5. Enhancement
De-interlacing by averaging

6. Binarisation

7. Skeletonisation Uses mathematical morphology
Iterative sequential thinning
Uses mathematical morphology
Direction and rotation invariant

8. Matching Techniques Image Overlay Simple: scaled images
Warp: new co-ordinate system
Vector Overlay
Neighbourhood scoring
Weighted Factor
Branch, curvature, intersection score
Correlations of these
Statistical
Discriminant analysis
Principal component analysis

9. Implementation Specifics
ImageJ
Open source
Improves on Java imaging
Provides basic image processing methods
Java
Cross-platform
Flexible and powerful
Only basic imaging support
Pattern Extraction and
Matching Interface

10. Demonstration Dual implementations Simple: aimed at end user
Functional: used for experimentation

11. Results

12. Conclusions Image preparation Image Analysis
Enhancement & binarisation successful
Skeletonisation can be improved
Segmentation should be automated
Image Analysis
Contour tracing successful
General feature extraction successful
Matching needs further investigation

13. Questions?