Measuring 3D shape similarity by matching the medial scaffolds

Chang, Ming-Ching; Kimia, Benjamin B.

doi:10.1109/iccvw.2009.5457437

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…The principal of geometric similarity and symmetry has been established in theory as a crucial shape description problem [16][17][18]. Recent studies [19,20] have been linked to measuring the distances between descriptors using the dissimilarity measurements to reduce the set of measured values and achieve accurate results. Mathematical generalization that satisfactorily represents the salient regions and shapes of any 3D structure is an imperative and also a starting point of research.…”

Section: Prior Workmentioning

confidence: 99%

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

et al. 2022

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This paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of salient regions in order to aid detection, distinguishing, measurement and restoration of real degradation and digitization errors. The case study is the digital 3D structure of the Camino Degli Angeli, in the Urbino’s Ducal Palace, acquired by the structure from motion (SfM) technique. In order to obtain an accurate, featured representation of the matching shape, the strong mesh processing computations are performed over the mesh surface while preserving real shape and geometric structure. In addition to perceptually based feature ranking, the new theoretical approach for ranking the evaluation criteria by employing neural networks (NNs) has been proposed to reduce the probability of deleting shape points, subject to optimization. Numerical analysis and simulations in combination with the developed virtual reality (VR) application serve as an assurance to restoration specialists providing visual and feature-based comparison of damaged parts with correct similar examples. The procedure also distinguishes mesh irregularities resulting from the photogrammetry process.

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Section: Prior Workmentioning

confidence: 99%

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

et al. 2022

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“…The main challenge to a content-based 3D model retrieval system is how to extract representative features for effectively discriminating different types of 3D models [1,2]. In general, 3D model retrieval methods can be classified into four categories: histogram-based descriptors [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], transform-based descriptors [20][21][22][23][24][25][26][27][28], 2D view-based descriptors , and graph-based descriptors [53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

“…Graph-based descriptors have the potential of describing the geometrical and topological properties of a 3D model in a more faithful way, particularly for deformable 3D models. The methods include extended cone-curvature [53], medial scaffold [54], Reeb graph [55], skeleton-based descriptors [56], etc.…”

Section: Introductionmentioning

confidence: 99%

3D Model Retrieval Using 3D-DFT Spectral and Cepstral Descriptors of Local Octants

Lee

Shih

Liu³

2015

Proceedings of the 3rd International Conference on Intelligent Systems and Image Processing 2015

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Conventional 3D-DFT features proposed for 3D model retrieval tried to extract the spectral/cepstral features from the spectrum/cepstrum of the 3D model, without considering the local properties of a 3D model. In this paper, local 3D-DFT spectral descriptor as well as local 3D-DFT cepstral descriptor will be proposed for 3D model retrieval. First, the set of voxels covering the surface of a 3D model is divided into 8 octants. 3D-DFT spectral descriptors and 3D-DFT cepstral descriptors are extracted from each octant and combined to form the local 3D-DFT spectral descriptor and local 3D-DFT cepstral descriptor. Experiments conducted on the Princeton Shape Benchmark (PSB) database have shown that the proposed local 3D-DFT descriptors outperform other 3D-DFT descriptors.

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“…Estimating the coordinates of the most similar volume can be the very pixels, features that correspond to human visual perception, texture features such as global descriptors or Gabor features, or local descriptors such as SIFT or corner detectors [2]. Shape features such as Fourier descriptors, moment invariants and finite element models were surveyed in [12], graph based shape features were presented in [1]. A method for retrieving 3D datasets based on Local Binary Patterns (LBP) features [4] was introduced in [11] and compared with features such as 3D Wavelet Transforms and 3D co-occurence matrices.…”

Section: Introductionmentioning

confidence: 99%

3D volume localization using miniatures

Šajn

Radojević

Dobravec

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Abstract-The prediction of the position of a given volume sample in a full body atlas, also known as a volume localization, is a part of an initial stage of image retrieval in most of the dedicated CAD systems. In this paper we present two methods for volume localization, namely histogram matching and classifier regression. Since the histogram matching method ignores the spatial orientation, it is used when the orientation of the volume cubes are not the same. On the other hand the classifier regression is much faster and can be used as a quick estimation and as a tool to reduce the scope of the initial problem. Both presented methods were tested on a dataset with 3962 volumes of a human body atlas. The accuracy and the speed of execution was compared and is presented in this paper.

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Measuring 3D shape similarity by matching the medial scaffolds

Cited by 5 publications

References 26 publications

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

3D Model Retrieval Using 3D-DFT Spectral and Cepstral Descriptors of Local Octants

3D volume localization using miniatures

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