Hierarchical Gabor filters for object detection in infrared images

This paper presents a new representation called “hierarchical Gabor filters” and associated novel local measures which are used to detect potential objects of interest in images. The “first stage” of the approach uses a wavelet set of wide-bandwidth separable Gabor filters to extract local measures from an image. The “second stage” makes certain spatial groupings explicit by creating small-bandwidth, non-separable Gabor filters that are tuned to elongated contours or periodic patterns. The non-separable filter responses are obtained from a weighted combination of the separable basis filters, which preserves the computational efficiency of separable filters while providing the distinctiveness required to discriminate objects from clutter. This technique is demonstrated on images obtained from a forward looking infrared (FLIR) sensor