An Evaluation and Improved Matching Cost of Stereo Matching Method

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Author(s)

Kapil S. Raviya 1,* Dwivedi Ved Vyas 2 Ashish M. Kothari 1

1. C. U. Shah University, Electronics Communication Engineering, Wadhwan City, 363030, India

2. Department of Electronics Communication Engineering, AITS, 360005, Rajkot, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2016.10.06

Received: 15 Jun. 2016 / Revised: 28 Jul. 2016 / Accepted: 6 Sep. 2016 / Published: 8 Oct. 2016

Index Terms

Stereo Matching, Disparity, Depth, Morphological operator, guided filter, Zero depth

Abstract

The main target of stereo matching algorithms is to find out the three dimensional (3D) distance, or depth of objects from a stereo pair of images. Depth information can be derived from images using disparity map of the same scene. There are many applications of computer vision like People tracking, Gesture recognition, Industrial automation and inspection, Security and Biometrics, Three-dimensional modeling, Web and Cloud, Aerial surveys etc. There are large categories of stereo algorithms which are used for finding the disparity or depth. This paper presents a proposed stereo matching algorithm to obtain depth map, enhance and measure. The hybrid mathematical process of the algorithm are color conversion, block matching, guided filtering, Minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last non linear spatial filtering. Our algorithm is produce noise less, reliable, smooth and efficient depth map. We obtained the results with ground truth image using Structural Similarity Index Map (SSIM) and Peak Signal to Noise Ratio (PSNR). 

Cite This Paper

Kapil S. Raviya, Dwivedi Ved Vyas, Ashish M. Kothari,"An Evaluation and Improved Matching Cost of Stereo Matching Method", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.10, pp.42-52, 2016. DOI: 10.5815/ijigsp.2016.10.06

Reference

[1]Jianbo Jiao, Ronggang Wang, "Local stereo matching with improved matching cost and disparity refinement," IEEE Computer Society, 1070-986X/14/2014, pp.16-27.

[2]S. Mukherjee, R. M. Guddeti, "A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo vision," IEEE, 978-1-4799-4665-5/14/ 2014.

[3]Saeed Mahmoudpour, Manbae Kim, "A novel depth estimation method using infocused and defocused images," 2014 IEEE International Conference on Consumer Electronics (ICCE), 978-1-4799-1291-9/14, pp.121-122.

[4]Ouk Choi, Seung-Won Jung, "A consensus-driven approach for structure and texture aware depth map upsampling" IEEE Transactions On Image Processing, Vol. 23, NO. 8, August 2014, pp. 3321-3335.

[5]Xiaoyan Hu, Philippos Mordohai, "A quantitative evaluation of confidence measures for stereo vision", IEEE transactions on pattern analysis and machine intelligence, 0162-8828/12, vol. 34, no. 11, November 2012, pp. 2121-2133.

[6]M. Baydoun, M. Adnan Al-Alaoui, "Enhancing stereo matching with classification," IEEE Access, Digital Object Identifier 10.1109/ACCESS.2014.2322101, Vol. 2,pp. 485-499, 2014.

[7]V. H. Borisagar, M. A. Zaveri, "Disparity map generation from illumination variant stereo images using efficient hierarchical dynamic programming," Hindawi Publishing Corporation,e Scientific World Journal, Vol. 2014,dx.doi.org/10.1155/2014/513417.

[8]R. Gupta, Siu-Yeung Cho, "Window-based approach for fast stereo correspondence," IET Comput. Vis., 2013, Vol. 7, Iss. 2, pp. 123–134, 10.1049/iet-cvi.2011.0077.

[9]S. Ibarra-Delgado, J. R. Cozar, "Low-textured regions detection for improving stereoscopy algorithms," IEEE, 978-1-4799-5313-4/14/2014, pp. 676-680.

[10]Jinwook Choi, Dongbo Min, "Reliability-based multiview depth enhancement considering interview coherence," IEEE transactions on circuits and systems for video technology, vol. 24, no. 4, april 2014,pp. 603-616.

[11]Wei Zhou, Yuchao Dai1, "Efficient depth estimation from single image," IEEE, China SIP 2014, 978-1-4799-5403-2/14, pp. 296-300

[12]Scharstein, Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," Int. J. Comput. Vis., 2002, pp. 7–42.

[13]http://vision.middlebury.edu/stereo/data/

[14]Kaiming He, Jian Sun, "Guided image filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 2013.

[15]Ashish M. Kothari, Ved Vyas Dwivedi, "Video watermarking – combination of discrete wavelet & cosine transform to achieve extra robustness," International Journal of Image, Graphics and Signal Processing (IJIGSP), ISSN 2074-9074, Online: ISSN 2074-9082, 2013.

[16]Ashish M. Kothari, Ved Vyas Dwivedi, "Hybridization of DCT and SVD in the Implementation and Performance Analysis of Video Watermarking," International Journal of Image, Graphics and Signal Processing (IJIGSP), Print: ISSN 2074-9074 & Online: ISSN 2074-9082, 2012.

[17]Ashish M. Kothari, Ved Vyas Dwivedi, "Video Watermarking – Embedding binary watermark into the digital video using hybridization of three transforms," International Journal of Signal and Image Processing Issues Vol. 2015, no. 1, pp. 9-17 ISSN: 2458-6498.