A method for finding the affine transformation which relates two projections of a planar curve, arbitrarily oriented in 3-space, is presented. The method is based on a canonic parameterization of curves and the decomposition of these curves into component ellipses such that an arbitrary linear transformation applied to the whole scene also applies to the component ellipses. The relationship between the ellipses allows for a simple solution for the affine transformation relating views of a contour without point correspondence information. Experimental results indicated the need for contour smoothing in the presence of noise; hence filtering in the Fourier domain was applied to the contours prior to parameterization. The theoretical development of this technique and experimental results are presented.

A method for determination of the affine transform relating projections of planar contour features is developed and tested experimentally. The method does not require knowledge of any point correspondences and is applicable to general affine transforms including those resulting from rotations of contours out of the plane of projection. This algorithm, together with a feature extraction procedure to derive contours (open or closed loops) from images of the complete object could form the basis of a system for determination of orientation and identity of a wide class of objects from 2-D views. The technique is based on the relation of moment tensors derived from two views of the planar contours, and determines the affine transform by solution of a set of linear equations.

A method is developed by which images resulting from orthogonal projection of rigid planar-patch objects arbitrarily oriented in three-dimensional (3-D) space may be used to form systems of linear equations which are solved for the affine transform relating the images. The technique is applicable to complete images and to unlabeled feature sets derived from images, and with small modification may be used to transform images of unknown objects such that they represent images of those objects from a known orientation, for use in object identification. No knowledge of point correspondence between images is required. Theoretical development of the method and experimental results are presented. The method is shown to be computationally efficient, requiring O(N) multiplications and additions where, depending on the computation algorithm, N may equal the number of object or edge picture elements.

The problem of identification of rigid planar-patch objects from images projected from random orientations in 3-D space is approached using tensor representation of image measures. This allows standardization of object orientation without knowledge of object identity, and hence permits a simple one-step comparison operation between the standardized unknown and each library image. Geometric transformations due to oblique viewing angles, as well as translation, rotation, and scale change are handled. Experimental results using a library of camera-acquired images demonstrate correct performance of the algorithm.

The effects of fading on the performance of a spread spectrum multiple access communication system using direct sequence modulation are investigated. Expressions for the system signal-signal-to-noise ratio and for the mean and variance of the desired and interfering users are derived in terms of the spreading code and fading correlation functions. The system error probability is also evaluated for the special case of slow Rayleigh fading. The results suggest that under conditions of fading and interference, long code sequences may be required to maintain low error rates.