Fast 3-D Feature Point Detector Based on Harris

2013 ◽  
Vol 325-326 ◽  
pp. 1567-1570 ◽  
Author(s):  
Jing Guo Lv ◽  
Wei Zhe Kong ◽  
Dong Yue Li
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Feature Point ◽  
Feature Points ◽  
Digital Photogrammetry ◽  
Key Technology ◽  
Harris Detector ◽  
The Right ◽  
Point Detector

Image matching is a key a key technology to be solved in the fields of digital photogrammetry and computer vision. Matching based on points matching is most widely used now. How to locate the right feature points is vital. Only accurate feature points can make right matching results. The paper introduces a method of 3-D Harris detector. The two steps matching have been done in our works. The results of experiments show that the 3-D Harris detector is accuracy and efficient.

An Application of 3-D Harris Operator in Video Frame Feature Detection

2014 ◽  
Vol 543-547 ◽  
pp. 2759-2762
Author(s):  
Jing Guo Lv ◽  
Wei Zhe Kong ◽  
Dong Yue Li
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Feature Detection ◽  
Video Frame ◽  
Feature Points ◽  
Digital Photogrammetry ◽  
Gaussian Smoothing ◽  
Harris Detector

In order to solve the problem of image matching, the points matching in the fields of digital photogrammetry and computer vision is researched very deeply. The accuracy of the feature points plays an important role in making right matching results. The paper introduces a method of 3-D Harris detector. An algorithm of Gaussian smoothing is applied for eliminating sudden gray change. Corners of all pixels are detected in a video using Harris. The results of experiments show that the 3-D Harris detector is accuracy and efficient.

Equi-affine differential invariants for invariant feature point detection

2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Differential Invariants ◽  
Feature Points ◽  
Affine Invariant ◽  
Moving Frames ◽  
Feature Detectors ◽  
Point Detection ◽  
Point Detector ◽  
Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

Binocular Robot Vision with Active Viewpoint for Sphere Measurement

2012 ◽  
Vol 516 ◽  
pp. 343-348
Author(s):  
Yoshito Yabuta ◽  
Hiroshi Mizumoto ◽  
Shiro Arii
Keyword(s):  
Smooth Surface ◽  
Vision System ◽  
Contact Point ◽  
Robot Vision ◽  
Feature Point ◽  
Feature Points ◽  
Spatial Coordinates ◽  
The Right

A binocular robot vision system reconstructs 3D scenes from right and left images by using triangulation. However when using triangulation, the corresponding problems have to be resolved. To resolve the corresponding problems easily, authors have proposed a binocular robot vision system with an autonomously moving active viewpoint. By using this active viewpoint, the system constructs the correspondence between images of feature points of an object on the right and left images and calculates the spatial coordinates of the feature points. However the system cannot achieve correspondence between right and left images for smooth surface without feature points like corners. In this paper, we propose a method to extract feature points on a smooth surface virtually. To extract the feature points on the smooth surface of an object virtually, we use an active viewpoint of our system and vergence motion of the right and left cameras. In this system, the right and left cameras viewpoints are corresponded mechanically by the active viewpoint and vergence motion of both cameras. As the virtual feature point, a contact point between the smooth surface and tangential line from the epipole on the baseline of right and left cameras is extracted from the camera images. Because the right and left cameras viewpoints are fixed by the active viewpoint, the active viewpoint becomes the constraint for the extraction of the virtual feature points. The information of the virtual feature points on the smooth surface of an object is used for the calculation of the spatial coordinates of the object. The effect of the proposed method to extract the feature points virtually is shown experimentally by using a sphere as an object.

Research on Digital Image Based Displacement Measurement Method

2012 ◽  
Vol 446-449 ◽  
pp. 3399-3404
Author(s):  
Nian Song Hong
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
High Precision ◽  
Digital Image ◽  
Measurement Method ◽  
Displacement Measurement ◽  
Feature Points ◽  
Digital Photogrammetry ◽  
Key Technology ◽  
Main Work

As the characteristics of high precision and non-contact measurement, digital photogrammetry technology is widely applied to displacement engineering measurement. On the basis of digital photogrammetry theory and the digital image processing technology, the main work of this paper is to find a method on displacement measurement method based on digital image which includes the key technology of feature points coordinates extraction from digital image and the algorithm of displacement measurement.

Image Feature Matching Based on SIFT Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4157-4161
Author(s):  
Xin Zhang ◽  
Ya Sheng Zhang ◽  
Hong Yao
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Quadratic Function ◽  
Scale Space ◽  
Feature Point ◽  
Image Feature ◽  
Three Dimension ◽  
Feature Points ◽  
Gradient Distribution ◽  
Sift Feature

In the process of image matching, it is involved such as image rotation, scale zooming, brightness change and other problems. In order to improve the precision of image matching, image matching algorithm based on SIFT feature point is proposed. First, the method of generating scale space is introduced. Then, the scale and position of feature points are determined through three dimension quadratic function and feature vectors are determined through gradient distribution characteristic of neighborhood pixels of feature points. Finally, feature matching is completed based on the Euclidean distance. The experiment result indicates that using SIFT feature point can achieve image matching effectively.

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.

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.

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

2011 ◽  
Vol 121-126 ◽  
pp. 701-704
Author(s):  
Xue Tong Wang ◽  
Yao Xu ◽  
Feng Gao ◽  
Jing Yi Bai
Keyword(s):  
Image Matching ◽  
Singular Value ◽  
Feature Points ◽  
Matching Algorithm ◽  
Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

Study on the Structural Model of Human Pose

2013 ◽  
Vol 464 ◽  
pp. 387-390
Author(s):  
Wei Hua Wang
Keyword(s):  
Computer Vision ◽  
Human Behavior ◽  
Structural Model ◽  
New Method ◽  
Domain Modeling ◽  
Key Technology ◽  
Broad Application ◽  
Structural Relation ◽  
Human Pose ◽  
Human Image

The analysis and understand of human behavior is broad application in the computer vision domain, modeling the human pose is one of the key technology. In order to simplify the model of the human pose and expediently describe the human pose, a lot of condition was appended to confine the process of human pose modeling or the application environments in the current research. In this paper, a new method for modeling the human pose was proposed. The human pose was modeled by the structural relation according to the physiological structural, the advantages of the model are the independent of move, the independent of scale of the human image and the dependent of view angle, it can be used to modeling the human behavior in video.

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