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1 March 1994 Multiclass 3D distortion-invariant object detection in clutter
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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 NT equals 36 training set images. The filter uses training objects in a constant background, correlation peak constraints on the NT 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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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