Abstract
This paper presents a new method for model-based object recognition which uses a single, comprehensive analytic object model representing the entirety of a suite of gray-scale views of the object. In this way, object orientation and identity can be directly established from arbitrary views, even though these views are not related by any geometric image transformation. The approach is also applicable to other real and complex sensed data, such as radar and thermal signatures. The unprocessed object model is comprised of a set of basis images with complex exponential harmonic terms as coefficients. A new model is formed comprised of the reciprocal set of the object basis set. The projection of an acquired image onto the reciprocal basis thus produces samples of a complex exponential, the phase of which reveals the pose parameters. Estimation of this phase for several degrees of freedom corresponds to the plane wave direction of arrival (DOA) problem; thus the pose parameters can be found using DOA solution techniques. Results are given which illustrate the performance of a simplified, preliminary, implementation of this method using real-world images.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Published Version
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