scholarly journals 3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

2021 ◽  
Vol 13 (11) ◽  
pp. 2145
Author(s):  
Yawen Liu ◽  
Bingxuan Guo ◽  
Xiongwu Xiao ◽  
Wei Qiu
Keyword(s):  
Real Data ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Denoising Method ◽  
3D Mesh ◽  
Mesh Denoising ◽  
Scene Structure ◽  
Sharp Edges ◽  
Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

Feature Matching Based on Triangle Guidance and Constraints

2018 ◽  
Vol 32 (08) ◽  
pp. 1855014 ◽  
Author(s):  
Hongmin Liu ◽  
Hongya Zhang ◽  
Zhiheng Wang ◽  
Yiming Zheng
Keyword(s):  
Feature Matching ◽  
High Accuracy ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Matching Method ◽  
Shared Feature ◽  
Triangular Structure ◽  
Circle Center ◽  
Image Transformations

For images with distortions or repetitive patterns, the existing matching methods usually work well just on one of the two kinds of images. In this paper, we present novel triangle guidance and constraints (TGC)-based feature matching method, which can achieve good results on both kinds of images. We first extract stable matched feature points and combine these points into triangles as the initial matched triangles, and triangles combined by feature points are as the candidates to be matched. Then, triangle guidance based on the connection relationship via the shared feature point between the matched triangles and the candidates is defined to find the potential matching triangles. Triangle constraints, specially the location of a vertex relative to the inscribed circle center of the triangle, the scale represented by the ratio of corresponding side lengths of two matching triangles and the included angles between the sides of two triangles with connection relationship, are subsequently used to verify the potential matches and obtain the correct ones. Comparative experiments show that the proposed TGC can increase the number of the matched points with high accuracy under various image transformations, especially more effective on images with distortions or repetitive patterns due to the fact that the triangular structure are not only stable to image transformations but also provides more geometric constraints.

scholarly journals Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7570
Author(s):  
Ziyan Zhang ◽  
Yan Liu ◽  
Jiawei Tian ◽  
Shan Liu ◽  
Bo Yang ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Feature Tracking ◽  
Feature Matching ◽  
Negative Impact ◽  
Three Dimensional ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
3D Surface Reconstruction ◽  
3D Surface

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

Vision for mobile robots

1992 ◽  
Vol 337 (1281) ◽  
pp. 341-350 ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Structure From Motion ◽  
Optic Flow ◽  
Image Sequences ◽  
Additional Constraint ◽  
Feature Points ◽  
Scene Structure ◽  
Corner Points ◽  
Edge Points

Localized feature points, particularly corners, can be computed rapidly and reliably in images, and they are stable over image sequences. Corner points provide more constraint than edge points, and this additional constraint can be propagated effectively from corners along edges. Implemented algorithms are described to compute optic flow and to determine scene structure for a mobile robot using stereo or structure from motion. It is argued that a mobile robot may not need to compute depth explicitly in order to navigate effectively.

scholarly journals ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS

2016 ◽  
Vol III-3 ◽  
pp. 333-338 ◽  
Author(s):  
J. Meidow ◽  
H. Hammer ◽  
M. Pohl ◽  
D. Bulatov
Keyword(s):  
Laser Scanning ◽  
Initial Point ◽  
Real Data ◽  
Boundary Representation ◽  
Geometric Constraints ◽  
Data Set ◽  
3D City Models ◽  
Building Models ◽  
Recognition And Enforcement ◽  
City Models

Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.

scholarly journals A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3978
Author(s):  
Tianyou Chu ◽  
Yumin Chen ◽  
Liheng Huang ◽  
Zhiqiang Xu ◽  
Huangyuan Tan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Local Features ◽  
Selection Method ◽  
Image Features ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Retrieval Task ◽  
Street View

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

scholarly journals A Multiscale Constraints Method Localization of 3D Facial Feature Points

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Hong-an Li ◽  
Yongxin Zhang ◽  
Zhanli Li ◽  
Huilin Li
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Distribution Model ◽  
Feature Points ◽  
Widespread Application ◽  
Point Distribution ◽  
Localization Accuracy ◽  
Localization Method ◽  
Relative Angle ◽  
Point Distribution Model

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.

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