Windage Yaw Angle Detection of Suspension Insulator Strings Based on Edge Feature Matching

2012 ◽  
Vol 241-244 ◽  
pp. 98-103
Author(s):  
Hai Jun Lu ◽  
Yu Xiang Lv ◽  
Wei Qing Ma ◽  
Xiao Long Zhao ◽  
Xue Lian Zheng
Keyword(s):  
Real Time ◽  
Hough Transform ◽  
Feature Matching ◽  
Matching Accuracy ◽  
A Algorithm ◽  
Generalized Hough Transform ◽  
Suspension Insulator ◽  
Depth Analysis ◽  
Yaw Angle ◽  
Edge Feature

By in-depth analysis and summary of tower suspension insulator strings images collected, a algorithm of edge feature matching of suspension insulator strings was proposed to detect the windage yaw angle in real-time. By filter, image grayscale, interframe difference and edge feature matching which based on invariance Generalized Hough Transform (IGHT) and local feature of suspension insulator strings stored in a database, the coordinates of the ends of suspension insulator strings were determined, and then the size of windage yaw angle of suspension insulator strings was calculated. The algorithm proposed can provide translation, scaling and rotation invariance, and be better matching accuracy and robustness.

Efficient and Robust Feature Matching via Local Descriptor Generalized Hough Transform

2013 ◽  
Vol 373-375 ◽  
pp. 536-540 ◽  
Author(s):  
Jing Li ◽  
Tao Yang
Keyword(s):  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Synthetic Image ◽  
Real World Data ◽  
Feature Correspondence ◽  
Local Descriptor ◽  
Generalized Hough Transform ◽  
Invariant Feature ◽  
Matching Performance

Robust and efficient indistinctive feature matching and outliers removal is an essential problem in many computer vision applications. In this paper we present a simple and fast algorithm named as LDGTH (Local Descriptor Generalized Hough Transform) to handle this problem. The main characteristics of the proposed method include: (1) A novel local descriptor generalized hough transform framework is presented in which the local geometric characteristics of invariant feature descriptors are fused together as a global constraint for feature correspondence verification. (2) Different from standard generalized hough transform, our approach greatly reduces the computational and storage requirements of parameter space through taking advantage of the invariant feature correspondences. (3) The proposed algorithm can be seamlessly embedded into the existing image matching framework, and significantly improve the image matching performance both in speed and robustness in challenge conditions. In the experiment we use both synthetic image data and real world data with high outliers ratio and severe changes in view point, scale, illumination, image blur, compression and noises to evaluate the proposed method, and the results demonstrate that our approach achieves achieves faster and better matching performance compared to the traditional algorithms.

Research on Matching Pair Purification Methods of Image Based on SIFT Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1851-1854 ◽  
Author(s):  
Qiang Li ◽  
Qi Yuan Sun
Keyword(s):  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Robot Vision ◽  
Equal Division ◽  
Matching Accuracy ◽  
Vision Navigation ◽  
Sift Algorithm ◽  
Purification Methods ◽  
Matching Pair

In view of the SIFT algorithm in image matching will produce a lot of mismatches, the paper has applied a method which is based on Hough Transform will remove the SIFT matching error effectively. Firstly, to use the SIFT algorithm finish the image matching roughly. And then, using the Hough Transform to form the equal division hough units. And according to the matching parameter to distribute all the match into the hough units. The match in the units which has least matching-pair will be deleted. Experimental results show that the method can effectively improve the matching accuracy of feature matching and it lays a foundation for the following robot vision navigation.

scholarly journals Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hui Wang ◽  
Meng Wang ◽  
Peng Zhao
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Mobile Devices ◽  
Hough Transform ◽  
Feature Matching ◽  
Tracking Algorithm ◽  
Sports Video ◽  
Detection And Tracking ◽  
Real Time Image ◽  
Time Image

Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.

Real-time pattern recognition using an optical generalized Hough transform

2015 ◽  
Vol 54 (36) ◽  
pp. 10586 ◽  
Author(s):  
Ariel Fernández ◽  
Jorge L. Flores ◽  
Julia R. Alonso ◽  
José A. Ferrari
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Hough Transform ◽  
Time Pattern ◽  
Generalized Hough Transform

Real-time object recognition using a modified generalized Hough transform

2003 ◽  
Vol 36 (11) ◽  
pp. 2557-2570 ◽  
Author(s):  
Markus Ulrich ◽  
Carsten Steger ◽  
Albert Baumgartner
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Hough Transform ◽  
Generalized Hough Transform

On the Performance Comparisons of Native and Clientless Real-Time Screen-Sharing Technologies

2021 ◽  
Vol 17 (2) ◽  
pp. 1-26
Author(s):  
Chun-ying Huang ◽  
Yun-chen Cheng ◽  
Guan-zhang Huang ◽  
Ching-ling Fan ◽  
Cheng-hsin Hsu
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Performance Metrics ◽  
Video Quality ◽  
Dynamic Network ◽  
Web Browsers ◽  
Depth Analysis ◽  
Measurement Methodology ◽  
Overall Performance ◽  
Screen Sharing

Real-time screen-sharing provides users with ubiquitous access to remote applications, such as computer games, movie players, and desktop applications (apps), anywhere and anytime. In this article, we study the performance of different screen-sharing technologies, which can be classified into native and clientless ones. The native ones dictate that users install special-purpose software, while the clientless ones directly run in web browsers. In particular, we conduct extensive experiments in three steps. First, we identify a suite of the most representative native and clientless screen-sharing technologies. Second, we propose a systematic measurement methodology for comparing screen-sharing technologies under diverse and dynamic network conditions using different performance metrics. Last, we conduct extensive experiments and perform in-depth analysis to quantify the performance gap between clientless and native screen-sharing technologies. We found that our WebRTC-based implementation achieves the best overall performance. More precisely, it consumes a maximum of 3 Mbps bandwidth while reaching a high decoding ratio and delivering good video quality. Moreover, it leads to a steadily high decoding ratio and video quality under dynamic network conditions. By presenting the very first rigorous comparisons of the native and clientless screen-sharing technologies, this article will stimulate more exciting studies on the emerging clientless screen-sharing technologies.

INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

2007 ◽  
Vol 21 (08) ◽  
pp. 1339-1351 ◽  
Author(s):  
ZHI-YONG LIU ◽  
HONG QIAO ◽  
LEI XU
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Line Segment ◽  
Reconstruction Error ◽  
Mean Square ◽  
Noise Resistance ◽  
Generalized Hough Transform ◽  
The Mean ◽  
Sample Reconstruction

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.

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