Optoelectronic 3-D Object Classification From 2-D Images
Chye-Hwa Loo, Atef Z. Elsherbeni · Journal of Lightwave Technology · 2008
The task of object classification is complicated by variations in the 3-D object, which translate into distortions in 2-D images. The pattern matching for 3-D invariance classification requires a large amount of data and computation time. Proposed here is an efficient 3-D object classification algorithm for real-time fringe-adjusted joint transform correlator (FJTC)-based automatic target recognition (ATR) system. The proposed classification technique employed a fragment-based recognition approach and a new type of synthetic discriminant function filter in the generation of distortion-invariant correlation filter sets. The optoelectronic FJTC is then used to provide correlation of the filter sets with the input under a proper arrangement. This classification method is simple and fast, hence is suitable to be in use by real-time ATR systems. For the conclusion, simulation results are provided to prove the effectiveness of the proposed system in the classification of objects invariant to 3-D out-of-plane rotation distortion.