21 September 1994 Object detection in the presence of clutter using Gabor filters
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In this paper the problem of detecting objects in the presence of clutter is studied. The images considered are obtained from both visual and infrared sensors. A feature-based segmentation approach to the object detection problem is pursued, where the features used 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 filters. A selection of these filtered images is made and each (selected) filtered 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' features, and their spatial locations, are inputted to a least squared error based clustering algorithm. This clustering algorithm yields a segmentation of the original image -- it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across the different spatial orientations, and frequencies. This method is applied on a number of visual and infrared images, every one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique set of texture `energy' features is typically associated with the segment containing the object(s) of interest.
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Nalini K. Ratha, Anil K. Jain, Sridhar Lakshmanan, "Object detection in the presence of clutter using Gabor filters", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186576; https://doi.org/10.1117/12.186576


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