Accession Number : ADA333426

Title :   Automatic Target Recognition and Indexing by Non-Orthogonal Image Expansion and Data-Dependent Normalization with Implementation.

Descriptive Note : Final rept. 1 Aug 83-31 Jul 97,

Corporate Author : ILLINOIS UNIV AT CHICAGO CIRCLE

Personal Author(s) : Ben-Arie, Jezekiel ; Atkin, G.

PDF Url : ADA333426

Report Date : 20 SEP 1997

Pagination or Media Count : 49

Abstract : This research is concerned with the development of a neural system for robust projective-invariant recognition of multiple targets which may be partially occluded in a cluttered background based on single gray-level images. For this purpose we have developed in the research a new method for affine-invariant iconic representation and recognition of targets using a novel set of Gabor/Fourier kernels with multi-dimensional indexing in the frequency domain. An affine-invariant representation of local image patches is extracted in the form of spectral signatures, by directly convolving the image with our novel configuration of these kernels. We achieved 100% correct recognition rates with a model library of 26 models over a wide range of viewing poses and distances (360 of rotation and tilt and 82 of slant and 4 octaves of scale). The system also maintains its 100% recognition rate in high levels of noise/clutter (up to -17 dB) and to resolution degradation (1:5 reduction). A novel method for representation and recognition of 3D Object/Targets based on 3D frequency domain representation was also developed and tested.

Descriptors :   *TARGET RECOGNITION, *SPECTRUM SIGNATURES, DEGRADATION, MODELS, RESOLUTION, CLUTTER, CONFIGURATIONS, IMAGES, FREQUENCY DOMAIN.

Subject Categories : Target Direction, Range and Position Finding
      Radiofrequency Wave Propagation

Distribution Statement : APPROVED FOR PUBLIC RELEASE