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Quadratic Gabor filters for object detection.

D M Weber1, D P Casasent

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. weber@stonethree.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces novel quadratic filters for improved object detection and classification. These filters create complex decision surfaces, outperforming traditional linear methods and offering computational savings for infrared vehicle detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Traditional correlation filters use linear decision surfaces, limiting detection and classification capabilities.
  • Developing advanced filters is crucial for enhancing performance in complex imaging scenarios.

Purpose of the Study:

  • To introduce a new class of quadratic filters for superior detection and classification.
  • To explore the creation of spherical, elliptical, hyperbolic, and linear decision surfaces.
  • To demonstrate computational savings through macro filter design.

Main Methods:

  • Filters are constructed by linearly combining basis filters into macro filters.
  • Magnitude square operations and weighted linear combinations create quadratic decision surfaces.

Related Experiment Videos

  • Gabor basis filters and an extended piecewise quadratic neural network (E-PQNN) were utilized and optimized.

    • Main Results:

    • The proposed quadratic filters achieve better detection and classification than linear filters.
    • Macro filter design significantly reduces computational load by minimizing correlation operations.
    • Methods for optimizing filter parameters, macro filter count, and combination coefficients were demonstrated.

    Conclusions:

    • The novel quadratic filters offer enhanced capabilities for object detection and classification tasks.
    • The approach provides a computationally efficient method for achieving complex decision surfaces.
    • Preliminary results show promise for infrared vehicle detection applications.