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.
Presentation Outline
• Problem Statement
• Objectives
• Approach
• Research Done
• Conclusion
Problem Statement
• Hair identification in Zoology and Forensics
• Subjectivity
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.
Approach to the Study:
• Lack of literature focused on hair recognition
• Multi-disciplinary nature
• New process designed
Approach to the Study:
Process Stages
Image
Capture
Sensor
Feature
Feature
Classifier
System
Generation
Selection
Design
Evaluation
• Each stage detailed next
Figure Adapted from Theodoris et al (2003:6)
Research Done:
Image Capture
•
How can hair pattern images be captured?
–
Based in Zoology Department
–
2 approaches considered
Light Microscope
SEM
Research Done:
Image Capture
Light Microscope
SEM
Scale Patterns
Cross Section
Patterns
Research Done:
Image Capture
• Scale Patterns
– Use SEM
– Better representation of texture in image
Light Microscope
SEM
Research Done:
Image Capture
• Cross section patterns
– Use Light microscope
– 2D shape preferred to a 3D shape
Light Microscope
SEM
Research Done:
Image Capture
• Decisions affecting design
– Scale patterns texture based
– Cross section patterns shape based
– 2 separate sub-processes
– Decision not to combine their results
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
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
Research Done:
Sensor Stage
Original
Thresholding
Edge Detection
Grab Cut + Thresholding
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
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
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
Research Done:
Feature Selection
What selection of features is necessary
•
Scale Pattern Processing
–
Image tessellation
–
Use of variance or average absolute deviation
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
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
Research Done:
Results
•
From implementation using:
– ImageJ plugins written in Java 1.4
– 25 scale patterns processed
– 50 cross section patterns processed
Research Done:
Results
Scale pattern results (Variance)
60%
% Correct Classifications
50%
40%
Best
Worst
Changes
30%
20%
10%
0%
4 Filters
8 Filters
Number of Filters
16 Filters
Research Done:
Results
Scale pattern results (AAD)
80%
% Correct Classifications
70%
60%
50%
Best Case
Worst Case
Changes
40%
30%
20%
10%
0%
4 Filters
8 Filters
Number of Filters
16 Filters
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
Research Done:
Results
Cross section pattern results
100%
90%
% Correct Classifications
80%
70%
60%
Euclidean
Hamming
50%
40%
30%
20%
10%
0%
Blue
Wildebeest
Impala
Jackal
Species
Springbok
Zebra
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
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.
Questions
•
•
•
•
•
•
Manual Preparation Work
Sensor
Feature extraction
Feature Selection
Classifier Design
Results