Object detection in the presence of clutter using Gabor filters

Nalini K. Ratha; Anil K. Jain; Sridhar Lakshmanan

doi:10.1117/12.186576

21 September 1994 Object detection in the presence of clutter using Gabor filters

Nalini K. Ratha, Anil K. Jain, Sridhar Lakshmanan

Proceedings Volume 2298, Applications of Digital Image Processing XVII; (1994) https://doi.org/10.1117/12.186576
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Nalini K. Ratha, Anil K. Jain, and Sridhar Lakshmanan "Object detection in the presence of clutter using Gabor filters", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); https://doi.org/10.1117/12.186576

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