<title>Model-based 3-D object recognition using Hermite transform and homotopy techniques</title>

Abstract

This paper presents a new method for model-based object recognition and orientation determination which uses a single, comprehensive analytic object model representing the entirety of a suite of images 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 object model is formed from 2-D Hermite function decompositions of an object image expanded about the angles of object rotation by Fourier series. A measure of error between the model and the acquired view is derived as an exact analytic expression, and is minimized over all values of the viewing angle by evaluation of a polynomial system of equations. The roots of this system are obtained via homotopy techniques, and directly provide object identity and orientation information. Results are given which illustrate the performance of this method for noisy real-world images acquired over a single viewing angle variation.