A parallel algorithm is presented for detecting dominant points on a digital closed curve. The procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve. The procedure first determines the region of support for each point based on its local properties, then computes measures of relative significance (e.g. curvature) of each point, and finally detects dominant points by a process of nonmaximum suppression. This procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but also on the precise determination of the region of support. This solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms. The inherent nature of scale-space filtering in the procedure is addressed, and the performance of the procedure is compared to those of several other dominant point detection algorithms, using a number of examples.>

A parallel algorithm for detecting dominant points on a digital closed curve is presented. The procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve. The procedure first determines the region of support for each point based on its local properties, then computes measures of relative significance (e.g. curvature) of each point, and finally detects dominant points by a process of nonmaxima suppression. This procedure leads to an important observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but mainly precise determination of the region of support. This solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms. The inherent nature of scale-space filtering in the procedure is addressed and the performance of the procedure is compared to those of several other dominant point-detection algorithms, using a number of examples.>

Various types of moments have been used to recognize image patterns in a number of applications. The authors evaluate a number of moments and addresses some fundamental questions, such as image representation ability, noise sensitivity, and information redundancy. Moments considered include regular moments, Legendre moments, Zernike moments, pseudo-Zernike moments, rotational moments and complex moments. Properties of these moments are examined in detail, and the interrelationships among them are discussed. Both theoretical and experimental results are presented.>

A local feature-aggregation method for recognizing two-dimensional objects based on their CAD models is presented. The method can handle cases in which the objects are translated, rotated, scaled and occluded, and it is well suited for parallel implementation. Two types of local features, the L structures and the U structures, are extracted from the input image and matched with those of a model to search for an object similar to the model. Each of the matches hypothesizes the locations of the object in the input image, and score (similarity measure) is computed and associated with the hypothesized location to indicate the probability of the match. Matches that hypothesize the same location will have the score associated with the location incremented. A cluster of hypothesized locations with high scores indicates the probable existence of the object in the input image.>

Various types of moments have been used to recognize image patterns in a number of applications. A number of moments are evaluated and some fundamental questions are addressed, such as image-representation ability, noise sensitivity, and information redundancy. Moments considered include regular moments, Legendre moments, Zernike moments, pseudo-Zernike moments, rotational moments, and complex moments. Properties of these moments are examined in detail and the interrelationships among them are discussed. Both theoretical and experimental results are presented.>