Harris Corner Detection Algorithm Optimization Based on OTSU

2018 ◽  
Vol 11 (2) ◽  
pp. 91-96
Author(s):  
Wang Lifang ◽  
Zhao Yanan ◽  
Qin Pinle ◽  
Gao Yuan
Keyword(s):  
Detection Algorithm ◽  
Corner Detection ◽  
Algorithm Optimization ◽  
Harris Corner ◽  
Harris Corner Detection

scholarly journals An Improved Corner Detection Algorithm Based on Harris

2012 ◽  
Vol 6-7 ◽  
pp. 717-721 ◽  
Author(s):  
Zhao Yang Zeng ◽  
Zhi Qiang Jiang ◽  
Qiang Chen ◽  
Pan Feng He
Keyword(s):  
Computation Time ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Test Results ◽  
Harris Corner ◽  
Texture Complexity ◽  
Harris Corner Detection ◽  
Better Than

In order to accurately extract corners from the image with high texture complexity, the paper analyzed the traditional corner detection algorithm based on gray value of image. Although Harris corner detection algorithm has higher accuracy, but there also exists the following problems: extracting false corners, the information of the corners is missing and computation time is a bit long. So an improved corner detection algorithm combined Harris with SUSAN corner detection algorithm is proposed, the new algorithm first use the Harris to detect corners of image, then use the SUSAN to eliminate the false corners. By comparing the test results show that the new algorithm to extract corners very effective, and better than the Harris algorithm in the performance of corner detection.

Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm

2018 ◽  
Vol 29 (6) ◽  
pp. 065004
Author(s):  
Yan Qin ◽  
Zhigang Dong ◽  
Renke Kang ◽  
Jie Yang ◽  
Babajide O Ayinde
Keyword(s):  
Cell Walls ◽  
Measurement Data ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Harris Corner ◽  
Honeycomb Cell ◽  
Harris Corner Detection

Yawning Detection Based on Mouth Feature Points Curve Fitting

2012 ◽  
Vol 605-607 ◽  
pp. 2227-2231
Author(s):  
Wu Yang Ding ◽  
Ling Zhang ◽  
Yun Hua Chen
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Curve Fitting ◽  
Detection Method ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Feature Points ◽  
Harris Corner ◽  
Fatigue Monitoring ◽  
Harris Corner Detection

A yawning detection method which can be used in drivers’ fatigue monitoring is proposed. To adapt to the variance in different mouth shapes and sizes, it based on mouth inner contour corner detection and curve fitting. First, the Harris corner detection algorithm was used to detect inner mouth feature points. Second, we established the open mouths’ mathematical model by curve fitting those points, calculated the degree of mouth openness using the mouth model, and generated the real-time M-curve. Third, the duration of big openness in successive images is divided into levels for further judgment. The validation results show that the method can obtain more precise mouth parameters and distinguish yawn from complex mouth activities. So the method achieves a higher level of accuracy.

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