Object detection using gabor filters

This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: A given image is passed through a bank of even-symmetric Gabor lters. A selection of these ltered images is made and each (selected) ltered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (\Gabor features"), and their spatial locations, are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image { it assigns to each pixel in the image a cluster label that identiies the amount of mean local energy the pixel possesses across diierent spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of \Ga-bor features" is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, ngerprint analysis, and image database query.