Quadratic Gabor filters for object detection
Date
1997
Authors
Weber David
Casasent David
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Abstract
We 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.
Description
Keywords
Algorithms, Correlation detectors, Infrared imaging, Neural networks, Object recognition, Optimization, Conjugate gradient method, Extended piecewise quadratic neural network (E PQNN), Gabor filters, Infrared vehicle detection, Digital filters
Citation
Proceedings of the South African Symposium on Communications and Signal Processing, COMSIG