A robust Graph Transformation Matching for non-rigid registration

Aguilar, Wendy; Frauel, Yann; Escolano, Francisco; Martínez-Pérez, M. Elena; Espinosa-Romero, A.; Lozano, Miguel Ángel

doi:10.1016/j.imavis.2008.05.004

Cited by 152 publications

(125 citation statements)

References 19 publications

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“…These are the outlier rejectors RANSAC [6] and Graph Transformation Matching (GTM ) [2] and, the graph matching method Dual-Step [4] (with the outlier detection scheme enabled). All the methods have been initialized with the matching by correlation (Corr ) results.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, a refinement process is often needed in order to remove erroneous correspondences in the tentative-set. This is the case of RANSAC [6] and Graph Transformation Matching [2], which remove outlying correspondences by enforcing some kind of global consistency. The main drawback of these methods is that their success strongly depends on the reliability of the tentative-set.…”

Section: Introductionmentioning

confidence: 99%

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Smooth Simultaneous Structural Graph Matching and Point-Set Registration

Sanromà

Alquézar

Serratosa

2011

Graph-Based Representations in Pattern Recognition

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Abstract. We present a graph matching method that encompasses both a model of structural consistency and a model of geometrical deformations. Our method poses the graph matching problem as one of mixture modelling which is solved using the EM algorithm. The solution is then approximated as a succession of assignment problems which are solved, in a smooth way, using Softassign. Our method allows us to detect outliers in both graphs involved in the matching. Unlike the outlier rejectors such as RANSAC and Graph Transformation Matching, our method is able to refine an initial the tentative correspondence-set in a more flexible way than simply removing spurious correspondences. In the experiments, our method shows a good ratio between effectiveness and computational time compared with other methods inside and outside the graphs' field.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smooth Simultaneous Structural Graph Matching and Point-Set Registration

Sanromà

Alquézar

Serratosa

2011

Graph-Based Representations in Pattern Recognition

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“…However, given the influence of complex geometric distortions, complex terrains, a large proportion of false matching points and others, partial false matching points in some remote sensing images cannot be detected by RANSAC. Aguilar et al (2009) proposed a graph transformation matching method which is used for non-rigid medical images with local distortions. GTM has the advantage of not requiring any model.…”

Section: Introductionmentioning

confidence: 99%

A Robust False Matching Points Detection Method for Remote Sensing Image Registration

Shan

Tang

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Image registration, False matching points detection, RANSAC, GTM, K-NN graph, SIFT ABSTRACT:Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest-neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

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“…Some examples are ICP (Besl and McKay, 1992), RANSAC (Brown and Lowe, 2003) and Graph Transformation Matching (Aguilar et al, 2009). What all these approaches have in common is that they use the geometrical information to reject a subset of erroneous matches (outliers).…”

Section: Introductionmentioning

confidence: 99%

“…More in the topic of the present paper, Aguilar et al (Aguilar et al, 2009) have recently presented an approach to use graph-based representations to the same end. To give some details, they build two K-nearestneighbour graphs with the keypoints of the two images that have been matched (i.e., edges are placed joining a keypoint with the K nearest neighbours in space).…”

Section: Introductionmentioning

confidence: 99%

GRAPH MATCHING USING SIFT DESCRIPTORS - An Application to Pose Recovery of a Mobile Robot

Sanromà

Alquézar

Serratosa

2010

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Image-feature matching based on Local Invariant Feature Extraction (LIFE) methods has proven to be successful, and SIFT is one of the most effective. SIFT matching uses only local texture information to compute the correspondences. A number of approaches have been presented aimed at enhancing the image-features matches computed using only local information such as SIFT. What most of these approaches have in common is that they use a higher level information such as spatial arrangement of the feature points to reject a subset of outliers. The main limitation of the outlier rejectors is that they are not able to enhance the configuration of matches by adding new useful ones. In the present work we propose a graph matching algorithm aimed not only at rejecting erroneous matches but also at selecting additional useful ones. We use both the graph structure to encode the geometrical information and the SIFT descriptors in the node's attributes to provide local texture information. This algorithm is an ensemble of successful ideas previously reported by other researchers. We demonstrate the effectiveness of our algorithm in a pose recovery application.

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A robust Graph Transformation Matching for non-rigid registration

Cited by 152 publications

References 19 publications

Smooth Simultaneous Structural Graph Matching and Point-Set Registration

Smooth Simultaneous Structural Graph Matching and Point-Set Registration

A Robust False Matching Points Detection Method for Remote Sensing Image Registration

GRAPH MATCHING USING SIFT DESCRIPTORS - An Application to Pose Recovery of a Mobile Robot

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