scholarly journals A Modified Harris Corner Detection for Breast IR Image

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chia-Yen Lee ◽  
Hao-Jen Wang ◽  
Chung-Ming Chen ◽  
Ching-Cheng Chuang ◽  
Yeun-Chung Chang ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Corner Detection ◽  
Feature Points ◽  
Low Contrast ◽  
Clustering Problem ◽  
Harris Corner ◽  
Image Contrast Enhancement ◽  
Harris Corner Detector ◽  
Intensity Resolution ◽  
Harris Corner Detection

Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

A Self-Adaptive Corner Detection Algorithm for Low-Contrast Images

2014 ◽  
Vol 615 ◽  
pp. 158-164
Author(s):  
Liang Sun ◽  
Jian Chun Xing ◽  
Shuang Qing Wang ◽  
Shi Qiang Wang
Keyword(s):  
Large Scale ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Feature Points ◽  
Low Contrast ◽  
Security Monitoring ◽  
Harris Corner ◽  
Comparative Results ◽  
Security Monitoring System ◽  
Self Adaptive

In order to effectively inhibit the image dithering caused by wind-induced vibration in the security monitoring system, it calls for the extraction and match of the feature points of the sequential frames. Harris corner detection algorithm is a widely-employed characteristics extraction algorithm in the image processing. In the security monitoring field, images and videos photographed are characterized by large scale, high pixel and low contrast degree. The classical algorithm often fails to effectively obtain the feature points while handling the images and videos of the kind. Concerning the above problems, this paper puts forward an improved self-adaptive corner detection algorithm. Firstly, this paper employs the self-adaptive gray threshold comparative results of the of every point with the surrounding eight neighborhood points to select the preselected points of part of the corners. Following that, this paper classifies the preselected points into three types according to certain rules and the value of the already selected self-adaptive gray threshold. At last, according to the classification results, this paper uses different corners to test function threshold and the preselected points as well to eliminate the peripheral points and the pseudo-corners so as to gain the genuine corners. After verifying the above improved algorithm in the practical scenario in the security monitoring, the results of this paper prove its effectiveness, feasibility and its advantages in terms of robustness.

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.

Feature Extraction Method Based on Moment Invariants

2014 ◽  
Vol 936 ◽  
pp. 2263-2266
Author(s):  
Wan Bing Li ◽  
Hong Wei Quan ◽  
Xia Fei Huang
Keyword(s):  
Detection Algorithm ◽  
Corner Detection ◽  
Reference Image ◽  
Feature Points ◽  
Moment Invariant ◽  
Feature Extraction Method ◽  
Harris Corner ◽  
Initial Control ◽  
Image Set ◽  
Harris Corner Detection

To match two or more images originated from the same scenario, a new fast automatic registration algorithm based on sparse feature point extraction is proposed. At the first step, the improved Harris corner detection algorithm is used to get two sets of feature points from the reference image and registration image. Second, a group of sparse feature points are selected from the reference image set as initial control points. Then, the corresponding matching points in the registration image set are searched based on local moment invariant similarity detection. Experimental results demonstrate that this method is fast and efficient.

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