Automated synthesis of distortion-invariant filters: AutoMinace

David Casasent; Rohit Patnaik

doi:10.1117/12.693228

2 October 2006 Automated synthesis of distortion-invariant filters: AutoMinace

David Casasent, Rohit Patnaik

Proceedings Volume 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision; 638401 (2006) https://doi.org/10.1117/12.693228
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

This paper presents our automated filter-synthesis algorithm for the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We discuss use of this autoMinace filter in face recognition and automatic target recognition (ATR), in which we consider both true-class object classification and rejection of non-database objects (impostors in face recognition and confusers in ATR). We use at least one Minace filter per object class to be recognized; a separate Minace filter or a set of Minace filters is synthesized for each object class. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (impostor/confuser/clutter rejection) performance. Our automated Minace filter-synthesis algorithm (autoMinace) automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that we achieve both good recognition and good impostor/confuser and clutter rejection performance; this is achieved using a training and validation set. No impostor/confuser, clutter or test set data is present in the training or validation sets. Use of the peak-to-correlation energy (PCE) ratio is found to perform better than the correlation peak height metric. The use of circular versus linear correlations is addressed; circular correlations require less storage and fewer online computations and are thus preferable. Representative test results for three different databases - visual face, IR ATR, and SAR ATR - are presented. We also discuss an efficient implementation of Minace filters for detection applications, where the filter template is much smaller than the input target scene.

Citation Download Citation

David Casasent and Rohit Patnaik "Automated synthesis of distortion-invariant filters: AutoMinace", Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 638401 (2 October 2006); https://doi.org/10.1117/12.693228

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