NSCT based computation of similarity measure for stereo image matching

被引：0

作者：

Zhang, Ka ^{[1
,2
,3
]}

Sheng, Yehua ^{[1
,2
,3
]}

Guan, Zhongcheng ^{[4
]}

Li, Jia ^{[1
]}

机构：

[1] Key Laboratory of Virtual Geographic Environment, MOE, Nanjing Normal University, Nanjing

[2] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing

[3] Key Laboratory of Police Geographic Information Technology Ministry of Public Security, Nanjing Normal University, Nanjing

[4] Qingdao Municipal Engineering Design & Research Institute Co. Ltd., Qingdao

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2015年 / 40卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Matching reliability; Nonsubsampled Contourlet transform; Stereo image matching; Structure feature; Weighted similarity measure;

D O I：

10.13203/j.whugis20130346

中图分类号：

学科分类号：

摘要：

A new weighted similarity measure based on nonsubsampled contourlet transform is proposed in this paper. In the new algorithm, high frequency sub-band parameters of left and right images in different scales and directions are firstly obtained by respectively carrying out NSCT to left and right images of stereopair. Secondly, according to high frequency sub-band parameters and gray levels in RGB channels of image, the computation model of weighted similarity measure between target window and searching window is designed. Lastly, utilizing standard stereo images, contrast experiments among proposed similarity measure and some known measures such as normalized correlation coefficient, etc.. Experimental results show that utilization of high frequency parameters of NSCT enhances the robustness of similarity measure and increases the reliability of stereo image matching. ©, 2015, Wuhan University. All right reserved.

引用

页码：457 / 461

页数：4

共 13 条

[1]

Yang H.C., Zhang S.B., Wang Y.B., Robust and Precise Registration of Oblique Images Based on Scale-invariant Feature Transformation Algorithm, IEEE Geoscience and Remote Sensing Letters, 9, 4, pp. 783-787, (2012)

[2]

Ji S., Fan D., Zhang Y., Et al., MVLL Multi-image Matching Model and Its Application in ADS40 Linear Array Images, Geomatics and Information Science of Wuhan University, 34, 1, pp. 28-31, (2009)

[3]

Snavely N., Seitz S.M., Szeliski R., Modeling the World from Internet Photo Collections, International Journal of Computer Vision, 80, 2, pp. 189-210, (2008)

[4]

Yang Q., Wang L., Ahuja N., A Constant-space Belief Propagation Algorithm for Stereo Matching, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010)

[5]

Scharstein D., Szeliski R., A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms, International Journal of Computer Vision, 47, 1-3, pp. 7-42, (2002)

[6]

Manap N.A., Soraghan J.J., Disparity Refinement Based on Depth Image Layers Separation for Stereo Matching Algorithms, Journal of Telecommunication, Electronic and Computer Engineering, 4, 1, pp. 51-64, (2012)

[7]

Lowe D.G., Distinctive Image Features from Scale-invariant Feature Transform, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)

[8]

El-Etriby S., Al-Hamadi A., Michaelis B., Dense Depth Map Reconstruction by Phase Difference -based Algorithm Under Influence of Perspective Distortion, International Journal Machine Graphics and Vision, 15, 3, pp. 349-361, (2006)

[9]

Cunha A.L., Zhou J.P., Do M.N., The Nonsubsampled Contourlet Transform: Theory, Design, and Applications, IEEE Transactions on Image Processing, 15, 10, pp. 3089-3101, (2006)

[10]

Yoon K.J., Kweon I.S., Adaptive Support-weight Approach for Correspondence Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 4, pp. 650-656, (2006)

← 1 2 →