Symbolic image matching by simulated annealing

Herault, Laurent; Horaud, Radu; Veillon, F.; Niez, Jean-Jacques

doi:10.5244/c.4.57

Cited by 35 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, various types of heuristic functions have been developed to prune the A* search space [Tsai and Fu 1979;Shapiro and Haralick 1981;Bunke and Allermann 1983;Sanfeliu and Fu 1983;Wong et al 1990]. Other methods such as simulated annealing [Herault et al 1990], neural networks [Feng et al 1994], probablistic relaxation [Christmas et al 1995], genetic algorithms [Wang et al 1997], and graph decomposition [Messmer and Bunke 1998] can also be used to reduce the computational cost. Observe that all of these optimization methods are developed for un-constrained matching where the matched subgraphs can assume any topology.…”

Section: Graph Matchingmentioning

confidence: 99%

Shape modeling and matching in identifying protein structure from low-resolution images

Abeysinghe

Chiu

et al. 2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

View full text Add to dashboard Cite

Figure 1: Identifying α-helices in a low-resolution protein image, using the Human Insulin Receptor -Tyrosine Kinase Domain (1IRK) as an example. The inputs are the amino-acid sequence of the protein (a), where α-helices are highlighted in green, and a density volume reconstructed from electron cryomicroscopy (b), where possible locations of α-helices have been detected as cylinders shown in (c). Our method computes the correspondence between the helices in the sequence and in the density volume (e). This is achieved by extracting a skeleton from the density volume shown in (d) and matching it with the sequence in (a). Note that the matching is error-tolerant therefore the resulting correspondence does not have to be a bijection. AbstractIn this paper, we describe a novel, shape-modeling approach to recovering 3D protein structures from volumetric images. The input to our method is a sequence of α-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of α-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.

show abstract

Section: Graph Matchingmentioning

confidence: 99%

Shape modeling and matching in identifying protein structure from low-resolution images

Abeysinghe

Chiu

et al. 2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

View full text Add to dashboard Cite

show abstract

“…A summary of the matching statistics is given in the first row of Table 1. Initial probabilities have been computed using equation (22). The effectiveness of matching is critically sensitive neither to the mean orientation parameter nor its variance.…”

Section: Matching Experimentsmentioning

confidence: 99%

“…Algorithms falling into this category can be divided into those that realise the matching process via discrete or configurational optimisation and those that adopt a continuous evidence combining framework. The former category includes the stochastic relaxation method of Herault et al (22) and the iterative discrete relaxation method of Wilson and Hancock. (16'17) The evidence combining approach is exemplified by probabilistic relaxation.…”

Section: T Introductionmentioning

confidence: 99%

Matching delaunay graphs

Finch

Wilson

Hancock

1997

Pattern Recognition

View full text Add to dashboard Cite

Abstract--This paper describes a Bayesian framework for matching Delaunay triangulations. Relational structures of this sort are ubiquitous in intermediate level computer vision, being used to represent both Voronoi tessellations of the image plane and volumetric surface data. Our matching process is realised in terms of probabilistic relaxation. The novelty of our method stems from its use of a support function specified in terms of face-units of the graphs under match. In this way, we draw on more expressive constraints than is possible at the level of edge-units alone. In order to apply this new relaxation process to the matching of realistic imagery requires a model of the compatibility between faces of the data and model graphs. We present a particularly simple compatibility model that is entirely devoid of free parameters. It requires only knowledge of the numbers of nodes, edges and faces in the model graph. The resulting matching scheme is evaluated on radar images and compared with its edge-based counterpart. We establish the operational limits and noise sensitivity on the matching of random-dot patterns. Copyright © 1996 Pattern Recognition Society. Published by Elsevier Science Ltd. Graph matchingRelaxation labelling Delaunay triangulation t. INTRODUCTIONDelaunay triangulations pervade computer vision. They not only provide a convenient and robust neighbourhood representation for Voronoi tessellations of the image plane, (1-3) but also provide a powerful geometric representation for volumetric information. (4-7) Ahuja (1-3) has promoted their use as a device for representing the early perceptual structure of dot patternsJ 8) Some of the computational advantages have been demonstrated by Tuceryan and Chorzempa (9) who show that the Delaunay graph has optimal stability to noise when compared with a number of alternative 2D neighbourhood graphs. Boissonnat (4) has established their effectiveness as a convenient volumetric surface representation in computational geometry. In the context of 3D vision, Faugeras et alJ 5) have identified the discontinuity preserving properties of Delaunay graphs and exploited them as a surface reconstruction device. More recently, triangulations have been exploited in the construction of deformable shape models. (7'6) Despite their widespread use and attractive computational features, little effort has been devoted to devising effective means for matching Delaunay graphs. This is surprising since the inexact graph matching problem has been the focus of sustained activity in a number of distinct methodological areas for over three decades. ~1°-17) One of the earliest practical matching techniques was the association graph idea of Barrow et alJ 18) In essence, this allowed subgraph isomorphisms to be located by transforming the matching process into one of searching for maximal cliques in an association graph. This idea was later exploited by Horaud and Skordas °1) for stereo matching. Symbolic scene descriptions have also been matched using consistent labelling techniques (14...

show abstract

“…In [27], the advantages and disadvantages of continuous optimization methods such as neural networks compared to the optimal backtracking methods are examined. Other continuous optimization approaches include the application of simulated annealing [28], genetic algorithms [29], [30], [31], and probabilistic relaxation [32].…”

Section: Introductionmentioning

confidence: 99%

Efficient subgraph isomorphism detection: a decomposition approach

Messmer

Bunke

2000

IEEE Trans. Knowl. Data Eng.

124

View full text Add to dashboard Cite

AbstractÐGraphs are a powerful and universal data structure useful in various subfields of science and engineering. In this paper, we propose a new algorithm for subgraph isomorphism detection from a set of a priori known model graphs to an input graph that is given online. The new approach is based on a compact representation of the model graphs that is computed offline. Subgraphs that appear multiple times within the same or within different model graphs are represented only once, thus reducing the computational effort to detect them in an input graph. In the extreme case where all model graphs are highly similar, the run-time of the new algorithm becomes independent of the number of model graphs. Both a theoretical complexity analysis and practical experiments characterizing the performance of the new approach will be given.

show abstract

Symbolic image matching by simulated annealing

Cited by 35 publications

References 15 publications

Shape modeling and matching in identifying protein structure from low-resolution images

Shape modeling and matching in identifying protein structure from low-resolution images

Matching delaunay graphs

Efficient subgraph isomorphism detection: a decomposition approach

Contact Info

Product

Resources

About