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EOS 513 Term Paper Presentation

EOS 513 Term Paper Presentation . G. Chen and T.D. Bui "Invariant Fourier-wavelet descriptor for pattern recognition," Pattern Recognition, vol. 32, pp. 1083-1088. Ashish Uthama Biomedical Signal and Image Computing Lab Department of Electrical and Computer Engineering

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EOS 513 Term Paper Presentation

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  1. EOS 513 Term Paper Presentation G. Chen and T.D. Bui "Invariant Fourier-wavelet descriptor for pattern recognition," Pattern Recognition, vol. 32, pp. 1083-1088 Ashish Uthama Biomedical Signal and Image Computing Lab Department of Electrical and Computer Engineering University of British Columbia

  2. The problem … • Pattern recognition: Classifying an object into predetermined categories • Applications: • Written character recognition • Object identification for unmanned vehicles • Content based image retrieval • …

  3. 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)

  4. Solution requirements … • Translation and scale invariant representation • Rotation invariant representation • Noise resistant

  5. Translation invariance • Achieved by changing the origin to the centroid (Centre of gravity/mass ) of the image

  6. Scale invariance • Achieved by normalizing in the polar coordinate system • ‘N’ concentric circles (radius = d*i/N)

  7. 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

  8. 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

  9. 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

  10. Images from the paper

  11. Results Table shows the performance of this approach using Haar wavelet basis.

  12. 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

  13. Critique • Novel, simple and intuitive! • Invariance of extracted features seems plausible (as demonstrated) • Computations/Comparisons for classification reduced • Easily extensible to 3D!

  14. Questions … Comments … ?

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