Image Mosaic Technology Based on Harris Corner Feature

2021 ◽  
pp. 111-120
Author(s):  
Xueya Liu ◽  
Shaoshi Wu ◽  
Dan Wang
Keyword(s):  
Image Mosaic ◽  
Harris Corner

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.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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