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30 October 1997 Adaptive wavelets for visual object detection and classification
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We investigate the application of adaptive wavelets for the representation and classification of signals in digitized speech and medical images. A class of wavelet basis functions are used to extract features from the regions of interest. These features are then used in an artificial neural network to classify the region are containing the desired object or belonging to the background clutter. The dilation and shift parameters of the wavelet functions are not fixed. These parameters are included in the training scheme. In this way the wavelets are adaptive to the expected shape and size of the signals. The results indicate that adaptive wavelet functions may outperform the classical fixed wavelet analysis in detection of subtle objects.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Farzin Aghdasi "Adaptive wavelets for visual object detection and classification", Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997);


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