ashish uthama eos 513 term paper presentation ashish uthama biomedical signal and image computing...

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


The problem …

Pattern recognition:

Classifying an object into predetermined categories

Applications: Written character recognition Object identification for unmanned vehicles Content based image retrieval …


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)


Solution requirements … Translation and scale invariant representation

Rotation invariant representation

Noise resistant


Translation invariance

Achieved by changing the origin to the centroid (Centre of gravity/mass ) of the image


Achieved by normalizing in the polar coordinate system

‘N’ concentric circles (radius = d*i/N)

Scale invariance


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


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


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


Images from the paper


Results

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


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


Critique

Novel, simple and intuitive! Invariance of extracted features seems plausible (as demonstrated) Computations/Comparisons for classification reduced

Easily extensible to 3D!


Questions … Comments … ?

ashish uthama eos 513 term paper presentation ashish uthama biomedical signal and image computing...

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