A versatile system for computer-controlled assembly

Ambler, A.P.; Barrow, Harry G.; Brown, C. M.; Burstall, Rod M.; Popplestone, Robin J.

doi:10.1016/0004-3702(75)90006-5

Cited by 150 publications

(69 citation statements)

References 5 publications

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“…Even after pruning, the computational costs associated with exhaustive techniques such as [1] would be prohibitive. It has been reported [3] that determining a maximum clique is analogous to finding the global maximum of a binary quadratic function.…”

Section: Solution Criteriamentioning

confidence: 99%

Recognizing Objects Using Color-Annotated Adjacency Graphs

Tu¹,

Saxena²,

Hartley³

1999

Shape, Contour and Grouping in Computer Vision

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Abstract. We introduce a new algorithm for identifying objects in cluttered images, based on approximate subgraph matching. This algorithm is robust under moderate variations in the camera viewpoints. In other words, it is expected to recognize an object (whose model is derived from a template image) in a search image, even when the cameras of the template and search images are substantially different. The algorithm represents the objects in the template and search images by weighted adjacency graphs. Then the problem of recognizing the template object in the search image is reduced to the problem of approximately matching the template graph as a subgraph of the search image graph. The matching procedure is somewhat insensitive to minor graph variations, thus leading to a recognition algorithm which is robust with respect to camera variations.

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Section: Solution Criteriamentioning

confidence: 99%

Recognizing Objects Using Color-Annotated Adjacency Graphs

Tu¹,

Saxena²,

Hartley³

1999

Shape, Contour and Grouping in Computer Vision

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“…Several algorithms have been written for finding maximal cliques 6 ' . It seems highly likely that the best of these is close to the optimum speed.…”

Section: Graph-theoretic Approaches To Object Locationmentioning

confidence: 99%

An Alternative to Graph Matching for Locating Objects from their Salient Features

Davies¹

1988

Procedings of the Alvey Vision Conference 1988

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means for locating objects in two dimensions. However, the technique has certain problems, since the maximal clique approach to graph matching which it employs can be excessively computation intensive. This raises the question of whether better results could be obtained by other means. Here we attempt to answer this question, and in particular to compare the LFF and GHT schemas. The actual comparison is carried out in section 4, sections 2 and 3 being devoted to respective preliminary studies of the two methods.The local-feature-focus method has become a standard means for robustly locating objects in two dimensions. Yet it is not without its difficulties, since the maximal clique approach to graph matching which it employs is excessively computation intensive, belonging to the class of NP-complete problems.Here we explore whether similar results can be obtained using other approaches, and in particular with the generalised Hough transform. The latter approach is found to be essentially equivalent to graph matching, while permitting objects to be located in polynomial (0(n )) time.

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“…The first approach views the matching problem as one of explicit search in state-space (see e.g., Messmer and Bunke, 1998;Shapiro and Haralick, 1981;Tsai and Fu, 1983). The pioneering work of Ambler et al (1973) falls into this class. Their approach is based on the idea that graph matching is equivalent to the problem of finding maximal cliques in the so-called association graph, an auxiliary graph derived from the structures being matched.…”

Section: Introductionmentioning

confidence: 99%