scholarly journals Identification of Primary Shape Descriptors on 3D Scanned Particles

2018 ◽  
Vol 62 (2) ◽  
pp. 59-64 ◽  
Author(s):  
Balázs Ludmány ◽  
Gábor Domokos
Keyword(s):  
Equilibrium Points ◽  
Shape Descriptor ◽  
Polyhedral Surface ◽  
Shape Descriptors ◽  
Main Difficulty ◽  
Automated Identification ◽  
Geophysical Research ◽  
3D Images ◽  
Physical Information ◽  
Global Equilibrium

The number of global mechanical equilibria as a shape descriptor (among others, for sedimentary particles) is at the forefront of current geophysical research. Although the technology is already available to provide scanned, 3D images of the particles (appearing as fine spatial discretization of smooth surfaces), nevertheless, the automated identification and measurement of global equilibria on such 3D images has not been solved so far. The main difficulty lies in the algorithmic distinction between local equilibria (associated with the small un-evenness of the pebble’s surface) and global equilibria, associated with the overall shape. The former are easily measured, however, only the latter provide meaningful physical information. Here we provide and illustrate an algorithm to detect global equilibrium points on a finely discretized, polyhedral surface provided by 3D scan of sedimentary particles.

Molecular Shape Descriptors. 1. Three-Dimensional Molecular Shape Descriptor

1985 ◽  
Vol 40 (11) ◽  
pp. 1108-1113 ◽  
Author(s):  
I. Motoc ◽  
G. R. Marshall ◽  
R. A. Dammkoehler ◽  
J. Labanowski
Keyword(s):  
Numerical Integration ◽  
Van Der Waals ◽  
Three Dimensional ◽  
Shape Descriptor ◽  
Molecular Shape ◽  
Shape Descriptors ◽  
Van Der Waals Volume

The paper presents and illustrates a method which uses numerical integration of the van der Waals envelope(s) to calculate with desired accuracy the molecular van der Waals volume and the three-dimensional molecular shape descriptor defined as the twin-number [OV(α, β); NOV(β, α), where OV and NOV represent the overlapping and, respectively, the nonoverlapping van der Waals volumes of the molecules α and ß superimposed according to appropriate criteria.

Alumina Sintering Kiln Flame Image Retrieval Based on ANN and Flame Shape Feature

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Hongliang Zhang ◽  
Jie Li ◽  
Zhong Zou
Keyword(s):  
Image Retrieval ◽  
Rotary Kiln ◽  
Shape Descriptor ◽  
High Accuracy ◽  
Retrieval Algorithm ◽  
Shape Descriptors ◽  
Shape Features ◽  
Retrieval Method ◽  
Flame Shape ◽  
Artificial Neural Network Ann

An alumina sintering rotary kiln flame image retrieval method was put forward based on artificial neural network (ANN) and flame shape features. An effective flame shape descriptor was introduced, based on which the flame image recognitions were carried out using ANN. Then, a flame image retrieval algorithm was designed. Experiments were carried out on the prototype machine with the flame images sampled from an alumina sintering rotary kiln. The results indicate that the shape descriptors can effectively describe the flame shapes and the proposed flame image retrieval method can achieve both high accuracy and efficiency. This method can be of promising theoretical and practical value for alumina sintering rotary kiln management and surveillance.

A new shape descriptor for surfaces in 3D images

2002 ◽  
Vol 23 (6) ◽  
pp. 703-711 ◽  
Author(s):  
Gabriella Sanniti di Baja ◽  
Stina Svensson
Keyword(s):  
Shape Descriptor ◽  
3D Images

scholarly journals An Application of Manifold Learning in Global Shape Descriptors

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 171 ◽  
Author(s):  
Fereshteh S. Bashiri ◽  
Reihaneh Rostami ◽  
Peggy Peissig ◽  
Roshan M. D’Souza ◽  
Zeyun Yu
Keyword(s):  
Manifold Learning ◽  
Sampling Rate ◽  
Shape Descriptor ◽  
Rapid Expansion ◽  
Shape Retrieval ◽  
Invariance Property ◽  
Surface Model ◽  
Shape Descriptors ◽  
Scale Invariant ◽  
Present Contribution

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.

scholarly journals Robust Affine Invariant Shape Descriptors

2021 ◽  
Author(s):  
Ye Mei
Keyword(s):  
Shape Descriptor ◽  
Zernike Moment ◽  
Shape Descriptors ◽  
Affine Invariant ◽  
Shape Features ◽  
Image Content ◽  
Shape Representations ◽  
Mellin Moment ◽  
Change Of Perspective ◽  
Invariant Shape

With the increasing number of available digital images, there is an urgent need of image content description to facilitate content based image retrieval (CBIR). Besides colour and texture, shape is an important low level feature in describing image content. An object can be photographed from different distances and angles. However, we often want to classify the images of the same object into one class, despite the change of perspective. So, it is desired to extract shape features that are invariant to the change of perspective. The shape of an object from one viewpoint to another can be linked through an affine transformation, if it is viewed from a much larger distance than its size along the line of sight. Those invariant shape features are known as affine invariant shape representations. Because of the change of perspective, it is more difficult to develop affine invariant shape representations than normal ones. The goal of this work is to develop affine invariant shape descriptors. Through shape retrieval experiments, we find that the performance of the existing affine invariant shape representations are not satisfactory. Especially, when the shape boundary is corrupted by noise, their performance degrades quickly. In this work, two new affine invariant contour-based shape descriptors, the ICA Fourier shape descriptor (ICAFSD) and the whitening Fourier shape descriptor (WFSD) have been developed. They perform better than most of the existing affine invariant shape representations, while having compact feature size and low computational time requirement. Four region-based affine-invariant shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD), the whitening Zernike moment shape descriptor (WZMSD), the ICA orthogonal Fourier Mellin moment shape descriptor (ICAOFMMSD), and the whitening orthogonal Fourier Mellin moment shape descriptor (WOFMMSD), are also proposed, in this work. They can be applied to both simple and complex shapes, and have close to perfect performance in retrieval experiments. The advantage of those newly proposed shape descriptors is even more apparent in experiments on shapes with added boundary noise: Their performance does not deteriorate as much as the existing ones.

scholarly journals Automated Identification of Fiducial Points on 3D Torso Images

2013 ◽  
Vol 5 ◽  
pp. BECB.S11800 ◽  
Author(s):  
Manas M. Kawale ◽  
Gregory P. Reece ◽  
Melissa A. Crosby ◽  
Elisabeth K. Beahm ◽  
Michelle C. Fingeret ◽  
...  
Keyword(s):  
Treatment Process ◽  
Breast Cancer Treatment ◽  
Color Information ◽  
Automated Identification ◽  
Algorithm Performance ◽  
3D Images ◽  
Breast Morphology ◽  
Physician Scientist ◽  
Novel Algorithms ◽  
2D And 3D

Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D coordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship.

scholarly journals DTW–RADON-BASED SHAPE DESCRIPTOR FOR PATTERN RECOGNITION

2013 ◽  
Vol 27 (03) ◽  
pp. 1350008 ◽  
Author(s):  
K. C. SANTOSH ◽  
BART LAMIROY ◽  
LAURENT WENDLING
Keyword(s):  
Pattern Recognition ◽  
Dynamic Time Warping ◽  
Recognition Performance ◽  
Shape Descriptor ◽  
Shape Descriptors ◽  
Time Warping ◽  
Recognition Method ◽  
Dynamic Time ◽  
Public Datasets ◽  
Comprehensive Study

In this paper, we present a pattern recognition method that uses dynamic programming for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalization based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behavior by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion.

scholarly journals Multiscale fractal dimension applied to tactical analysis in football: A novel approach to evaluate the shapes of team organization on the pitch

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256771
Author(s):  
Murilo José de Oliveira Bueno ◽  
Maisa Silva ◽  
Sergio Augusto Cunha ◽  
Ricardo da Silva Torres ◽  
Felipe Arruda Moura
Keyword(s):  
Retrieval System ◽  
Shape Descriptor ◽  
Shape Descriptors ◽  
Fractal Curve ◽  
Novel Approach ◽  
Image Retrieval System ◽  
Different Shapes ◽  
Team Organization ◽  
Measures Of Effectiveness ◽  
Practical Demonstration

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P@n and R@n). The MFD (P@5, 0.46±0.37 and P@10, 0.40±0.31, p < 0.001; R@5, 0.14±0.13 and R@10, 0.24±0.19, p < 0.001) and AllD (P@5 = 0.43±0.36 and P@10 = 0.39±0.32, p < 0.001; R@5 = 0.13±0.11 and R@10 = 0.24±0.20, p < 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptor in relation to all others, making it possible to apply this descriptor in the analysis of professional football matches.

Sign in / Sign up

Export Citation Format

Share Document