scholarly journals Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features

Medicine ◽  
2020 ◽  
Vol 99 (37) ◽  
pp. e22189
Author(s):  
Hongfei Sun ◽  
Jianhua Yang ◽  
Rongbo Fan ◽  
Kai Xie ◽  
Conghui Wang ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Harris Corner ◽  
Iterative Threshold

Adaptive Harris corner detection algorithm based on iterative threshold

2017 ◽  
Vol 31 (15) ◽  
pp. 1750181 ◽  
Author(s):  
Zhicheng Wang ◽  
Rong Li ◽  
Zhihao Shao ◽  
Mengxin Ma ◽  
Jianhui Liang ◽  
...  
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Corner Detection ◽  
Threshold Selection ◽  
Harris Corner ◽  
Simulation Results ◽  
Threshold Setting ◽  
Harris Corner Detection ◽  
Iterative Threshold

An adaptive Harris corner detection algorithm based on the iterative threshold is proposed for the problem that the corner detection algorithm must be given a proper threshold when the corner detection algorithm is extracted. In order to avoid the phenomenon of clustering and restrain the pseudo corner, this algorithm realizes the adaptive threshold selection by iteration instead of the threshold value of the Harris corner detection algorithm. Simulation results show that the proposed method achieves good results in terms of threshold setting and feature extraction.

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.

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

2010 ◽  
Vol 36 (7) ◽  
pp. 951-959 ◽  
Author(s):  
Bo LIU ◽  
Jian-Hua HUANG ◽  
Xiang-Long TANG ◽  
Jia-Feng LIU ◽  
Ying-Tao ZHANG
Keyword(s):  
Image Segmentation ◽  
Probability Density ◽  
Ultrasound Image ◽  
Density Difference ◽  
Gray Level ◽  
Ultrasound Image Segmentation

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method
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