scholarly journals A Multiresolution Stereo Matching Based on Genetic Algorithm using Edge Information

2010 ◽  
Vol 17B (1) ◽  
pp. 63-68
Author(s):  
Seok-Keun Hong ◽  
Seok-Je Cho
Keyword(s):  
Genetic Algorithm ◽  
Stereo Matching ◽  
Edge Information

Non-Local Stereo Matching Algorithm Based on Color and Edge Information

2020 ◽  
Vol 57 (10) ◽  
pp. 101020
Author(s):  
马晴晴 Ma Qingqing ◽  
王彩芳 Wang Caifang
Keyword(s):  
Stereo Matching ◽  
Edge Information ◽  
Matching Algorithm ◽  
Non Local

Stereo matching using genetic algorithm with adaptive chromosomes

2001 ◽  
Vol 34 (9) ◽  
pp. 1729-1740 ◽  
Author(s):  
Kyu-Phil Han ◽  
Kun-Woen Song ◽  
Eui-Yoon Chung ◽  
Seok-Je Cho ◽  
Yeong-Ho Ha
Keyword(s):  
Genetic Algorithm ◽  
Stereo Matching

Image Adaptive Denoising Method Based on Compressive Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 993-996
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng ◽  
Sun Li
Keyword(s):  
Genetic Algorithm ◽  
Image Quality ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Experimental Results ◽  
New Method ◽  
Denoising Method ◽  
Edge Information ◽  
Fast Speed ◽  
Adaptive Denoising

We present a new adaptive denosing method using compressive sensing (CS) and genetic algorithm (GA). We use Regularized Orthogonal Matching Pursuit (ROMP) to remove the noise of image. ROMP algorithm has the advantage of correct performance, stability and fast speed. In order to obtain the optimal denoising effect, we determine the values of the parameters of ROMP by GA. Experimental results show that the proposed method can remove the noise of image effectively. Compared with other traditional methods, the new method retains the most abundant edge information and important details of the image. Therefore, our method has optimal image quality and a good performance on PSNR.

Improved Genetic Algorithm to Calibration of the Pulse Coupled Neural Network Parameter

2013 ◽  
Vol 694-697 ◽  
pp. 1993-1997
Author(s):  
Rui Cheng Feng ◽  
Hai Yan Li ◽  
Zhi Yuan Rui ◽  
Rui Sheng Chen ◽  
Bao Cheng Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Recognition ◽  
Motion Detection ◽  
Recognition Accuracy ◽  
Improved Genetic Algorithm ◽  
Network Parameter ◽  
Edge Information ◽  
Quality Of Motion

Based on the research of PCNN algorithm,details the basic idea of the genetic algorithm, introduction of genetic algorithm for optimal family.In order to verify the effectiveness of the algorithm,implements the algorithm and comparison algorithm on the PC with the VS2008, CUDA, OpenCV 2.2.The experimental results showed that:Improved PCNN algorithm to better deal with the multi-modal regional background,improve the accuracy of moving object segmentation.Effectively filter interference prospects at the same time,complete retention of the shape, the edge information of the moving object.In this paper, the improved genetic algorithm can guarantee the quality of motion detection, and provide a guarantee for the the subsequent image recognition accuracy.

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