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Abstract : |
Both from a mathematical as well as a biological perspective, Wavelet transforms present themselves as an attractive means for extracting low-level information from an image. We present a processor-time optimal algorithm to implement wavelet transforms on a VLSI implementable parallel digital architecture. 1 Why 2-D Wavelet Transforms? A desirable initial processing step in an artificial vision system is the identification of certain attributes in an incoming image. Traditionally, this is done by transforming the image into a domain where these attributes or features emerge as coefficients corresponding to a set of expansion functions. Transforms in computer vision have been used for image analysis, segmentation, feature extraction and representation, data compression and object recognition. Image transforms can be classified on the basis of the resolution they provide in the visual space domain versus the spatial frequency domain. While the pixel by pixel grey level descripition of the image provides good resolution in the space domain, it gives no information about the spatial frequencies in the image. On the other hand, the Fourier Transform provides excellent resolution in the frequency domain but is spread over all space. It is often desirable to have good localization in both, the spatial as well as the frequency domains. High resolution in space makes the system robust to global distortions while high resolution in frequency makes it robust to high frequency noise. In 1946, Gabor [3] proved that no image transform can achieve an, |
