Abstract
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
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