Quadratic Gabor filters for object detection

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dc.contributor.authorWeber David
dc.contributor.authorCasasent David
dc.date.accessioned2011-05-15T15:57:30Z
dc.date.available2011-05-15T15:57:30Z
dc.date.issued1997
dc.description.abstractWe present a new class of quadratic filters that are capable of creating spherical, elliptical, hyperbolic and linear decision surfaces which result in better detection and classification capabilities than the linear decision surfaces obtained from correlation filters. Each filter comprises of a number of separately designed linear basis filters. These filters are non-linearly combined using an algorithm called the extended piecewise quadratic neural network (E-PQNN). In this work, we consider the use of Gabor basis filters; the Gabor filter parameters are separately optimized; the fusion parameters to combine the Gabor filter outputs are optimized using the conjugate gradient method; they and the non-linear combination of filter outputs are included in our E-PQNN algorithm. We present results obtained for an infra-red (IR) vehicle detection problem.
dc.description.versionConference Paper
dc.identifier.citationProceedings of the South African Symposium on Communications and Signal Processing, COMSIG
dc.identifier.urihttp://hdl.handle.net/10019.1/10441
dc.subjectAlgorithms
dc.subjectCorrelation detectors
dc.subjectInfrared imaging
dc.subjectNeural networks
dc.subjectObject recognition
dc.subjectOptimization
dc.subjectConjugate gradient method
dc.subjectExtended piecewise quadratic neural network (E PQNN)
dc.subjectGabor filters
dc.subjectInfrared vehicle detection
dc.subjectDigital filters
dc.titleQuadratic Gabor filters for object detection
dc.typeConference Paper
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