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The Scientific World Journal
Volume 2014, Article ID 513417, 12 pages
Research Article

Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

1Computer Engineering Department, Government Engineering College, Gandhinagar, Gujarat 382028, India
2Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India

Received 19 April 2014; Revised 27 August 2014; Accepted 7 September 2014; Published 20 October 2014

Academic Editor: Andrea Torsello

Copyright © 2014 Viral H. Borisagar and Mukesh A. Zaveri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair.