Fast matching of large point sets under occlusions

McAuley, Julian; Caetano, Tibério

doi:10.1016/j.patcog.2011.05.008

Cited by 20 publications

(15 citation statements)

References 21 publications

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“…On the other hand, Huh et al [46] develop a method for detecting a corresponding point pair between a polygonal object pair with a string matching method based on a confidence region model of a line segment. Another common approach is that of isometric point pattern matching [47]. According to these authors, in such a case, we assume that a "copy" of an object G (the "template") appears isometrically transformed within G´(the "target").…”

Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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“…Aiger and Kedem gave an asymptotically faster algorithm for rigid transformation and proposed novel algorithms for homothetic and similarity transformations [7]. McAuley and Caetano [8] specified a graphical model to solve the large point-set matching problem and adapted the model to handle occlusions. For non-rigid point-set matching, an algorithm was suggested to map nonrigid point-sets through using the joint estimation of thin-plate spline in [9].…”

Section: Introductionmentioning

confidence: 99%

Affine Invariant Point-Set Matching Using Convex Hull Bisection

Wang

Gao

et al. 2016

2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Affine invariant point-set matching is an important issue in computer vision and pattern recognition. Using reference points derived from the convex hull of the point-set is an existing idea to solve this problem. However, how to choose proper and enough reference points for extracting affine invariant and powerful discriminative descriptors is an open problem. In this paper, a novel method termed convex hull bisection (CHB) is proposed for affine invariant point-set matching. In CHB, for each point in the point-set, two reference points are derived by bisecting the convex hull using the line connecting the point and the centroid of the convex hull. The resulting reference points and other reference points, such as the centroid point of the convex hull and the mean point of the point-set, are utilised to yield an 11-dimensional affine invariant feature vector associated with any point within the point-set. The obtained point based descriptors are used for point-set matching. The proposed method works well even in the case that reference points are overlapping or collinear, which overcomes the limitations of the existing convex hull based methods. The effectiveness of the proposed method is validated by an extensive experimental investigation.

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“…Graph matching has been strongly used in pattern recognition and computer vision to study similarities among data structures representing objects [32]- [34]. The need of matching procedures relying to other criteria than Euclidean distances has generated a wide field of research about the definition of similarity measures between graphs [32], [35], optimization techniques [36]- [38], and structured estimations [39]. In the proposed algorithm, the graphs of the two domains are matched using a procedure aiming at maximizing their similarity, while at the same time preserving the structure of the transformed graph.…”

Section: Introductionmentioning

confidence: 99%

Graph Matching for Adaptation in Remote Sensing

Tuia

Muñoz-Marí

Gómez-Chova

et al. 2013

IEEE Trans. Geosci. Remote Sensing

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Abstract-We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering two acquisition conditions in a source and a destination domains, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal VHR image classification and angular effects compensation, show the validity of the proposed method to match related domains and enhance the application of cross-domains image processing techniques.

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Fast matching of large point sets under occlusions

Cited by 20 publications

References 21 publications

A Polygon and Point-Based Approach to Matching Geospatial Features

A Polygon and Point-Based Approach to Matching Geospatial Features

Affine Invariant Point-Set Matching Using Convex Hull Bisection

Graph Matching for Adaptation in Remote Sensing

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