Shape Retrieval Through Mahalanobis Distance With Shortest Augmenting Path Algorithm

Muruganathan, S.; Devarajan, N.; Chitra, D.; Manigandan, T.

doi:10.3844/jcssp.2014.552.562

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…As we are using the X-means to extracting the CLC set, the vectors descriptors do not have the same size for each model, so the Euclidean distance is not valid for our method. There are two distances that could adapt with our descriptors, the hausdorff distance and the Earth Mover Distance (EMD) (Rubner et al, 2000;Muruganathan et al, 2014;Edy et al, 2014). The EMD seems very expensive in terms of computation, then hausdorff distance is the most adaptable with the proposed vectors descriptors, also, it is the most used in this kind of problem.…”

Section: Similarity Measuringmentioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.Keywords: Characteristics Level Cut, Vector Descriptor, 3D Shape Indexing and Retrieval, X-means Algorithm, Similarity Measuring INTROCUCTIONRecently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al., 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al., 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them. The extraction of information of the 3D object in a robust and efficient way Recently, the technological development of 3D has become a real challenge. The proposed method permits to deal with this problem. Given a 3D object, the method starts by a normalization step of the processed object using the CPCA technique. Then, we extract a set of cuts based on our cutting method. This set of cuts (binary images) will be indexed by a 2D image descriptor to construct the descriptor vector associated to the 3D model, using the Hausdorff distance to calculate the distance between the vectors descriptors which allows us to measure the similarity between the 3D objects.The proposed descriptor shows the robustness in terms of response to the query provided by the user and the comparative study between the proposed descriptor and two other descriptors well known of the 3D/3D approach, the 3D Zernike moments and 3D invariant moments shows considerable results.

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Section: Similarity Measuringmentioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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“…In recent years a strong improvement of Computerbased methods for retrieving shapes from single shaded images can be documented (Remondino and El-Hakim, 2006;Stylianou and Lanitis, 2009;Muruganathan et al, 2014). This is particularly true when dealing with simplified 3D models (Algabary et al, 2014;Vani et al, 2012), such as virtual bas-relief representations (also named 2.5D models).…”

Section: Introductionmentioning

confidence: 99%

Improved Interactive Method for Reco-Vering 2.5D Models From Single Images

Furferi¹,

Governi²,

Puggelli³

et al. 2014

Journal of Computer Science

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In the last years the development of interactive Computer-based methods for building virtual and physical 2.5D models from single shaded images faced with an exponential growth. In particular, a wide range of methods based on image processing-based procedures and on Shape From Shading (SFS) can be documented. On the basis of the most favorable techniques devised in literature, the present work describes an improved interactive method capable of retrieving 2.5D models using image shading information. The pro-posed method performs a SFS-based reconstruction where (1) the overall geometry of the expected surface is first recovered and (2) the final 2.5D reconstruction is obtained by minimizing a suitable functional using the rough surface as an initialization function. The method improves previous interactive works by introducing a novel two-step rough surface recovery and a new definition of a functional to be minimized for solving the SFS problem. Tested against a set of case studies the proposed method proves to be effective in providing 2.5D models.

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Generalized unsupervised functional map learning for dense correspondence

Han

Shi

et al. 2023

Vis Comput

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Shape Retrieval Through Mahalanobis Distance With Shortest Augmenting Path Algorithm

Cited by 3 publications

References 20 publications

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Improved Interactive Method for Reco-Vering 2.5D Models From Single Images

Generalized unsupervised functional map learning for dense correspondence

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