image pattern recognition the identification of animal species through the classification of hair...

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 MoyoSupervisors: 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

SensorFeature

Generation

Feature

Selection

Classifier

Design

System

Evaluation

Figure Adapted from Theodoris et al (2003:6)

Image Capture

• Each stage detailed next

Research Done:

• How can hair pattern images be captured?

– Based in Zoology Department

– 2 approaches considered

Image Capture

SEMLight Microscope

Research Done:Image Capture

SEMLight Microscope

Scale Patterns

Cross Section Patterns

• Scale Patterns– Use SEM– Better representation of texture in image

Research Done:Image Capture

SEMLight Microscope

• Cross section patterns– Use Light microscope– 2D shape preferred to a 3D shape

Research Done:Image Capture

SEMLight Microscope

• 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

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)

0%

10%

20%

30%

40%

50%

60%

4 Filters 8 Filters 16 Filters

Number of Filters

% C

orr

ect

Cla

ssif

icati

on

s

Best

Worst

Changes

Research Done: Results

Scale pattern results (AAD)

0%

10%

20%

30%

40%

50%

60%

70%

80%

4 Filters 8 Filters 16 Filters

Number of Filters

% C

orr

ect

Cla

ssif

icati

on

s

Best Case

Worst Case

Changes

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

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BlueWildebeest

Impala Jackal Springbok Zebra

Species

% C

orr

ect

Cla

ssif

icati

on

s

Euclidean

Hamming

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