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1. Introduction
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This chapter describes an algorithm that robustly locates collections of salient line segments in an image. In computer vision and related applications, we often wish to find objects based on stored models from an image containing objects of interest [1 6]. To achieve this, a model-based object recognition system
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Computer-Aided Intelligent Recognition Techniques and Applications 2005 John Wiley & Sons, Ltd Edited by M. Sarfraz
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Fast Object Recognition Using DP
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first extracts sets of features from the scene and the model, and then it looks for matches between members of the respective sets. The hypothesized matches are then verified and possibly extended to be useful in various applications. Verification can be accomplished by hypothesizing enough matches to constrain the geometrical transformation from a 3D model to a 2D image under perspective projection. We first extract junctions formed by two lines in the input image, and then find an optimal relation between the extracted junctions, by comparing them with previously constructed model relations. The relation between the junctions is described by a collinear constraint and parallelism can also be imposed. Junction detection acts as a line filter to extract salient line groups in the input image and then the relations between the extracted groups are searched to form a more complex group in an energy minimization framework. The method is successfully applied to images with some deformation and broken lines. Because the system can define a topological relation that is invariant to viewpoint variations, it is possible to extract enough lines to guide 2D or 3D object recognition. Conventionally, the DP-based algorithm as a search tool is an optimization technique for the problems where not all variables are interrelated simultaneously [7 9]. In the case of an inhomogeneous problem, such as object recognition, related contextual dependency for all the model features always exists [10]. Therefore, DP optimization would not give the true minimum. On the other hand, the DP method has an advantage in greatly reducing the time complexity for a candidate search, based on the local similarity. Silhouette or boundary matching problems that satisfy the locality constraint can be solved by DP-based methods using local comparison of the shapes. In these approaches, both the model and matched scene have a sequentially connected form of lines, ordered pixels, or chained points [11 13]. In some cases, there also exist many vision problems, in which the ordering or local neighborhood cannot be easily defined. For example, definition of a meaningful line connection in noisy lines is not easy, because the object boundary extraction for an outdoor scene is itself a formidable job for object segmentation. In this chapter, we do not assume known boundary lines or junctions, rather, we are open to any connection possibilities for arbitrary junction groups in the DP-based search. That is, the given problem is a local comparison between predefined and sequentially linked model junctions and all possible scene lines in an energy minimization framework. Section 2 introduces previous research about feature grouping in object recognition. Section 3 explains a quality measure to detect two line junctions in an input image. Section 4 describes a combination model to form local line groups and how junctions are linked to each other. Section 5 explains how related junctions are searched to form the salient line groups in a DP-based search framework. Section 6 gives a criterion to test the collinearity between lines. Section 7 tests the robustness of the junction detection algorithm by counting the number of detected junctions as a function of the junction quality and whether a prominent junction from a single object is extracted under an experimentally decided quality threshold. Section 8 presents the results of experiments using synthetic and real images. Finally, Section 9 summarizes the results and draws conclusions.
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