The design and development of binarized composite binary phase only filters for three-dimensional image identification is discussed in this paper. A composite binary phase only filter based algorithm is developed to detect an unknown image from the pre-constructed database. The angular position of the unknown image is found by a cross correlation technique. The formulation of composite filters reduces the number of correlation operations necessary for detection.

This paper investigates the role of composite filters in reducing the search time for 3D model based object recognition. When one moves from 2D to 3D, one is faced with a huge amount of information to deal with. The composite filter combined with a multistage scheme is developed for processing this huge information. The design scheme for the composite filters is also elaborated. The procedures discussed in this paper demonstrate how detection of these various model images might help formulate a new metric for recognition performance.

In this paper, a novel metric is defined that will allow one to compare the performance of 3-D pattern recognition systems. Any real object is inherently, three-dimensional. Therefore, any input object for an automated target recognition system should be ideally compared to the 3-D information about the object. The proposed metric captures the essence of such comparisons.