Multiclass 3D distortion-invariant object detection in clutter

Gregory P. House; David P. Casasent

doi:10.1117/12.169412

1 March 1994 Multiclass 3D distortion-invariant object detection in clutter

Gregory P. House, David P. Casasent

Proceedings Volume 2237, Optical Pattern Recognition V; (1994) https://doi.org/10.1117/12.169412
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

We consider distortion-invariant filters for detection (i.e. to locate a number of different object classes). For each object, there are two different depression angles, four different contrast ratios, and 18 different aspect views. The objects are present in a variety of different real background clutter. One filer is able to recognize (detect) all 2 X 4 X 18 X 5 equals 720 object versions in clutter with no false alarms using N_T equals 36 training set images. The filter uses training objects in a constant background, correlation peak constraints on the N_T objects, and minimizes a weighted combination of the correlation plane energy due to the distortion spectrum and a noise spectrum. The new object and noise models used produce this excellent performance with no false class clutter training.

Citation Download Citation

Gregory P. House and David P. Casasent "Multiclass 3D distortion-invariant object detection in clutter", Proc. SPIE 2237, Optical Pattern Recognition V, (1 March 1994); https://doi.org/10.1117/12.169412

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