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Languages: English
Types: Unknown
Subjects: Q1, T1

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Current depth capturing devices show serious drawbacks in certain applications, for example ego-centric depth recovery: they are cumbersome, have a high power requirement, and do not portray high resolution at near distance. Stereo-matching techniques are a suitable alternative, but whilst the idea behind these techniques is simple it is well known that recovery of an accurate disparity map by stereo-matching requires overcoming three main problems: occluded regions causing absence of corresponding pixels; existence of noise in the image capturing sensor and inconsistent color and brightness in the captured images. We propose a modified version of the Census-Hamming cost function which allows more robust matching with an emphasis on improving performance under radiometric variations of the input images.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] A. Klaus, M. Sormann and K. Karner Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In International Conference on Pattern Recognition (ICPR), 2005.
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    • [3] R.Zabih and J. Woodfill, “Non-parametric Local Transforms for Computing Visual Correspondence.” in Proceeding of the European Conference of Computer Vision. Stockholm, Sweden, May 1994, pp. 151-158.
    • [4] U. Stilla, F. Rottensteiner, H. Mayer, B. Jutzi, M. Butenuth “Photogrammetric Image Analysis” Springer, New York, pp160-161
    • [5] Oren, M. and S. K. Nayar, Generalization of the Lambetian model and implications for machine vision, Int.J.Comp. Vision 14, pp.227-251, 1995.
    • [6] Heiko Hirschmüller and Daniel Scharstein (2009), Evaluation of Stereo Matching Costs on Images with Radiometric Differences, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 31(9), September 2009, pp. 1582-1599.
    • [7] “Middlebury stereo website” [Accessed 9th June 2014]. Available: http://vision.middlebury.edu/stereo/data/
    • [8] M. Fischler and R. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp. 381- 395, June 1981.
    • [9] G Egnal “Mutual Information as a Stereo Correspondence Measure,” Technical Report MS-CIS-00-20, Uni. Of Pennsylvania, 2000.
    • [10] H. Hirschmüller and D. Scharstein (2007), Evaluation of Cost Functions for Stereo Matching, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 18- 23 June 2007, Minneapolis, Minnesota, USA.
    • [11] “Stereoscopy website” [Accessed 19th June 2014]. Available: http://www.stereoscopy.com/faq/aerial.html
    • [12] “Image Synthesis website” [Accessed 29th June 2014]. Available: http://homepages.inf.ed.ac.uk/rbf/HIPR2/noise.htm
    • [13] H. Moravec, “Toward automatic visual obstatcle avoidance,” in Proceeding of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, August 1977, pp.584-590
    • [14] H. Hirschmuller, “Stereo processing by semi-global matching and mutual information,” in IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 871-878.
    • [15] Xin Luan, Honghong Zhou, Fangjie Yu, Xiufang Li, Bing Xue, and Dalei Song: A robust Local Census-based Stereo matching insensitive to Illumination changes, Proc. of International conference on Information and Automation (ICIA), 2012.
    • [16] Froba, B., Ernst, A. “Face detection with the modified Census Transform,” In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition. IEEE Computer Society Press, Los Alamitos (2004).
    • [17] “Siddhant Ahuja's webpage” [Accessed 9th June 2014] Available: http://siddhantahuja.wordpress.com/
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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