Chu J.,Nanchang Hangkong University |
Chu J.,Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition |
Gong W.,Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition |
Miao J.,Nanchang Hangkong University |
And 3 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2015
The traditional dynamic programming stereo matching algorithm can effectively guarantee the precision of matching and improve the running speed; but the depth of the parallax figure has the obvious stripes phenomenon, at the same time the low texture region and edge of the image have higher mismatch. For these problems, the paper proposes a new tree structure based on the linear filtering dynamic programming stereo matching algorithm. The algorithm firstly uses an improved adjustable parameters adaptive measure function to combine the color and gradient information of the matching images. Secondly, it uses the left image to guide the figure to filter the price of stereo matching. Thirdly, it utilizes the two direction simple tree structure dynamic programming optimization; and finally uses the parallax refinement method to get the final parallax figure. Theoretical analysis and experimental results have showed that the proposed algorithm can not only effectively eliminate the stripes phenomenon of dynamic programming algorithm but also improve the mismatch of the low texture area and the edge of the image. Copyright © 2015 Acta Automatica Sinica. All rights reserved.
Yuan J.,Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition |
Pei S.,Nanchang Hangkong University |
Ping Z.,Nanchang Hangkong University |
Yang D.,Nanchang Hangkong University |
Zhen C.,Nanchang Hangkong University
Proceedings - 2015 Chinese Automation Congress, CAC 2015 | Year: 2015
Compressed sensing theory by developing a signal sparse features, under the condition of far less than the Nyquist sampling rate, the correct signal is acquired with random sampling the discrete samples, and then through the nonlinear reconstruction algorithm reconstruction signal of high probability. © 2015 IEEE.