scholarly journals Research of Image Mosaic Algorithm Based on Harris Corner Detection

2015 ◽  
Author(s):  
Yun Zhang ◽  
Ji Yang ◽  
Lei Guang Lv ◽  
Guo Sun
Keyword(s):  
Corner Detection ◽  
Image Mosaic ◽  
Harris Corner ◽  
Harris Corner Detection

scholarly journals Image Mosaic Method Based on SIFT Features of Line Segment

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Zhu ◽  
Mingwu Ren
Keyword(s):  
Line Segment ◽  
Corner Detection ◽  
Image Mosaic ◽  
Scale Invariant ◽  
Point Matching ◽  
Lighting Condition ◽  
Harris Corner ◽  
Key Points ◽  
Scale Invariant Feature ◽  
Harris Corner Detection

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

An Improved Harris Corner Detection Algorithm for Noised Images

2012 ◽  
Vol 433-440 ◽  
pp. 6151-6156 ◽  
Author(s):  
Xin Zhang ◽  
Xiu Hua Ji
Keyword(s):  
Image Registration ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Corner Detection ◽  
Detection Accuracy ◽  
Image Mosaic ◽  
Harris Corner ◽  
Noise Impact ◽  
Corner Points ◽  
Harris Corner Detection

The Harris corner detection algorithm is widely applied in image mosaic, which is simple and stable. However, the algorithm has a disadvantage that it obtains a lot of false corners when there exist some noise in an image. An improved Harris corner detection algorithm is proposed in this paper. The new algorithm reduces the noise impact greatly. The experimental results show that the improved algorithm not only reduces false corner points greatly, but also retain the majority of true corners. As a result, it improves the detection accuracy and reduces the chance of error matching in image registration.

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