This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

Given the enormous number of available methods for finding polygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Instead, we suggest assessing an algorithm's results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the application of fifteen algorithms to a curve first used by Teh and Chin [31]; their ISEs are assessed relative to the optimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation can be used to select an appropriate assessment criterion.

Abstract-A system for interpretation of images of paper-based line drawings is described. Since a typical drawing contains both test strings and graphics, an algorithm has been developed to locate and separate text strings of various font size, style, and orientation. This is accom-plished by applying the Hough transform to the centroids of connected components in the image. The graphics in the segmented image is pro-cessed to represent thin entities by their core-lines and thick objects by their boundaries. The core-lines and boundaries are segmented into straight line segments and curved lines. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Such a description includes the location and attributes of simple po-lygonal shapes, circles, and interconnecting lines, and a description of the spatial relationships and occlusions among them. Hatching and fill-ing patterns are also identified. The performance of the system is eval-uated using several test images and the results are presented. The su-periority of these algorithms in generating meaningful interpretations of graphics, compared to conventional data compression schemes, is clear from these results. Index Terns-Document image analysis, drawing conversion, fea-ture extraction, graphics recognition, image understanding, knowl-edge-based systems, line-drawing interpretation, pattern recognition, text segmentation, vectorization. I.

We present a methodology for the quantitative performance evaluation of detection algorithms in computer vision. A common method is to generate a variety of input images by varying the image parameters and evaluate the performance of the algorithm as algorithm parameters vary. Operating curves that relate the probability of mis-detection and false alarm are generated for each parameter setting. Such an analysis does not integrate the performance of the numerous operating curves. In this paper we outline a methodology for summarizing many operating curves into a few performance curves. This methodology is adapted from the human psychophysics literature and is general to any detection algorithm. The central concept is to measure the effect of variables in terms of the equivalent effect of a critical signal variable; which in turn facilitates the determination of the breakdown point of the algorithm. We demonstrate the methodology by comparing the performance of two line detection algorit...

We first describe a multiresolutional discretization scheme for the boundary of a planar connected shape, by means of the Haar wavelet transform. This scheme yields a hierarchy of polygons of increasing complexity that adaptively captures the shape's boundary at increasing resolutions. Next, we present a new morphological transform called the Chordal Axis Transform for planar shapes that leads to a constructive definition of the skeleton of a discretized shape. We describe an efficient, adaptive algorithm based on the Constrained Delaunay Triangulation of polygons for skeletonizing the shape. The resulting skeleton is connected and faithfully captures the structure of the shape. Finally, we obtain a simple quantitative descriptor of the morphology of a discretized shape based on its Delaunay triangulation, and introduce the concept of morphological congruence. This leads to a graph theoretic characterization of the nature of structural differences between shapes that are morphologicall...

Abstract. We propose a simple approach to evolution of polygonal curves that is specially designed to t discrete nature of curves in digital images. It leads to simpli cation of shape complexity withnoblurring (i.e., shape rounding) e ects and no dislocation of relevant features. Moreover, in our approach the problem to determine the size of discrete steps for numerical implementations does not occur, since our evolution method leads in a natural way to a nite number of discrete evolution steps which are just the iterations of a basic procedure of vertex deletion.

Automatic extraction of facial feature points is one of the main problems for semantic coding of videophone sequences at very low bit rates. In this contribution, an approach for estimation of the eye and mouth corner point positions is presented. For this proposition, the location informations of the face model are exploited to define search areas for estimation of the eye and mouth corner point positions. Then, the eye and mouth corner point positions are estimated based on a template matching technique with eye and mouth corner templates. Finally, in order to verify these estimated corner point positions, some geometric conditions between the corner point positions and the center point positions of the eyes and the mouth are exploited. The proposed algorithm has been applied to test sequences Claire and Miss America with a spatial resolution corresponding to CIF and a frame rate of 10 Hz. Keywords: Facial feature points, eye and mouth corner point positions, face model, template mat...

Abstract—This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector’s parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided. Index Terms—Document analysis, graphics recognition and interpretation, vectorization, curve segmentation, performance evaluation, line drawings. æ 1

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covariance matrices