ashish uthama eos 513 term paper presentation ashish uthama biomedical signal and image computing...
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Ashish Uthama http://bisicl.ece.ubc.ca/
EOS 513 Term Paper Presentation
Ashish Uthama
Biomedical Signal and Image Computing Lab
Department of Electrical and Computer Engineering
University of British Columbia
G. Chen and T.D. Bui "Invariant Fourier-wavelet descriptor for pattern recognition," Pattern Recognition, vol. 32, pp. 1083-1088
Ashish Uthama http://bisicl.ece.ubc.ca/
The problem …
Pattern recognition:
Classifying an object into predetermined categories
Applications: Written character recognition Object identification for unmanned vehicles Content based image retrieval …
Ashish Uthama http://bisicl.ece.ubc.ca/
What’s in it for me?
My problem:
Try to find if there is a significant difference two groups of 3 dimensional distributions. Quantify this difference.
Similarities between the problem domains: Sparse representation of the object Sparse enough to significantly speed up the computations Complete enough to discriminate between important differences Use this representation to classify (differentiate)
Ashish Uthama http://bisicl.ece.ubc.ca/
Solution requirements … Translation and scale invariant representation
Rotation invariant representation
Noise resistant
Ashish Uthama http://bisicl.ece.ubc.ca/
Translation invariance
Achieved by changing the origin to the centroid (Centre of gravity/mass ) of the image
Ashish Uthama http://bisicl.ece.ubc.ca/
Achieved by normalizing in the polar coordinate system
‘N’ concentric circles (radius = d*i/N)
Scale invariance
Ashish Uthama http://bisicl.ece.ubc.ca/
Rotational invariance
Analyzing the data along polar angle axis
Rotation results in circular shift of signals along this axis 1-D Fourier transform results in features that are invariant under
rotations
Ashish Uthama http://bisicl.ece.ubc.ca/
Feature extraction
Apply wavelet transform along the radial direction (after 1-D Fourier)
Multiresolution representation Haar, Daubechies-4, Coiflet-3 and Symmlet-8 basis tried with no
much difference in performance Coarse coefficients aggregate at the center
Ashish Uthama http://bisicl.ece.ubc.ca/
Classification
Number of coefficients are small in coarse scale and increase with scale
Use the wavelet coefficients to locate a match progressively
At each scale: If only one match found : STOP (object classified) If none match : STOP (object can not be classified) If more than one match: Repeat at next scale
Efficient, Reduces number of entries to search
Ashish Uthama http://bisicl.ece.ubc.ca/
Images from the paper
Ashish Uthama http://bisicl.ece.ubc.ca/
Results
Table shows the performance of this approach using Haar wavelet basis.
Ashish Uthama http://bisicl.ece.ubc.ca/
Critique
Image parameters and algorithm parameters (N, angular resolution, database size/content) not mentioned in the results
Performance under noise not evaluated (Effects all steps) Effect of Quantization/ Re-sampling (while converting to polar)
errors not clear Details of comparing coefficients not presented (Distance between
coefficients?) Handling of different number of samples along the angular direction
not clarified
Ashish Uthama http://bisicl.ece.ubc.ca/
Critique
Novel, simple and intuitive! Invariance of extracted features seems plausible (as demonstrated) Computations/Comparisons for classification reduced
Easily extensible to 3D!
Ashish Uthama http://bisicl.ece.ubc.ca/
Questions … Comments … ?