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In this paper, we investigate the problem of dominant point detection on digital curves which consists in finding points with local maximum curvature. Thanks to previous studies of the decomposition of curves into sequence of discrete structures [1–3] , namely maximal blurred segments of width ν [4] , an initial algorithm has been proposed in [5] to detect dominant points. However, an heuristic strategy is used to identify the dominant points. We now propose a modified algorithm without heuristics but a simple measure of angle. In addition, two applications are as well presented: (1) polygonal simplification to reduce the number of detected dominant points by associating a weight to each of them, and (2) classification using the polygon issued from the reduced dominant points. The experimental results demonstrate the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Rigid transformations are involved in a wide range of digital image processing applications. When applied on discrete images, rigid transformations are usually performed in their associated continuous space, requiring a subsequent digitization of the result. In this article, we propose to study rigid transformations of digital images as fully discrete processes. In particular, we investigate a combinatorial structure modelling the whole space of digital rigid transformations on arbitrary subset of of size N × N. We describe this combinatorial structure, which presents a space complexity and we propose an algorithm enabling to construct it in linear time with respect to its space complexity. This algorithm, which handles real (i.e., non-rational) values related to the continuous transformations associated to the discrete ones, is however defined in a fully discrete form, leading to exact computation. [Copyright &y& Elsevier]