STEREO MATCHING ALGORITHM BASED ON ILLUMINATION CONTROL TO IMPROVE THE ACCURACY
DOI:
https://doi.org/10.5566/ias.1369Keywords:
computer vision, digital image processing, disparity map, gradient matching, pixel-based matching, stereo matching algorithmAbstract
This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.References
De-Maeztu L, Villanueva A, Cabeza R (2011). Stereo matching using gradient similarity and locally adaptive support-weight. Pattern Recogn Lett 32:1643-51.
Dominguez-Morales M, Cerezuela-Escudero E, Jimenez-Fernandez A, Paz-Vicente R, Font-Calvo JL, Inigo-Blasco P, textit{et al.} (2011). Image matching algorithms in stereo vision using address-event-representation: A theoretical study and evaluation of the different algorithms. Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP), 2011 Jul 18-21; Seville, Spain. 1-6.
Einecke N, Eggert J (2013). Anisotropic median filtering for stereo disparity map refinement. Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), 2013 Feb 21-24; Barcelona, Spain. 189-98.
Fernandes H, Costa P, Filipe V, Hadjileontiadis L, Barroso J (2010). Stereo vision in blind navigation assistance. Proceedings of the World Automation Congress (WAC), 2010 Sep 19-23; Kobe, Japan. 1-6.
Gu Z, Su X, Liu Y, Zhang Q (2008). Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recogn Lett 29:1230-5.
Gupta RK, Cho S (2010). Real-time stereo matching using adaptive binary window. Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission, 2010 May 17-20; Paris, France. 735-9.
Hamzah RA, Ibrahim H (2016). Literature survey on stereo vision disparity map algorithms. J Sensors 2016:1-23.
He K, Sun J, Tang X (2013). Guided image filtering. IEEE T Pattern Anal 35:1397-409.
Hirschmuller H, Innocent PR, Garibaldi J (2002). Real-time correlation-based stereo vision with reduced border errors. Int J Comput Vision 47:229-46.
Hosni A, Bleyer M, Gelautz M (2013). Secrets of adaptive support weight techniques for local stereo matching. Comput Vis Image Und 117:620-32.
Hu W, Zhang K, Sun L, Li J, Li Y, Yang S (2011). Virtual support window for adaptive-weight stereo matching. Proceedings of the Visual Communications and Image Processing (VCIP), 2011 Nov 6-9; Tainan, Taiwan. 1-4.
Huang H, Wang Q (2010). A region and feature-based matching algorithm for dynamic object recognition. Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010 Oct 29-31; Xiamen, China. 735-9.
Humenberger M, Engelke T, Kubinger W (2010). A census-based stereo vision algorithm using modified semi-global matching and plane fitting to improve matching quality. Proceedings of the Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 Jun 13-18; San Francisco, USA. 77-84.
Jung HY, Park H, Park IK, Lee KM, Lee SU (2014). Stereo reconstruction using high-order likelihoods. Comput Vis Image Und 125:223-36.
Kowalczuk J, Psota ET, Perez LC (2013). Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences. IEEE T Circ Syst Vid 23:94-104.
Lin Y, Lu N, Lou X, Zou F, Yao Y, Du Z (2013). Matching cost filtering for dense stereo correspondence. Math Probl Eng 2013:1-11.
Liu T, Dai X, Huo Z, Zhu X, Luo L (2012). A cost construction via MSW and linear regression for stereo matching. Proceedings of the International Conference on Pattern Recognition (ICPR), 2012 Nov 11-15; Tsukuba, Japan. 914-7.
Lu J, Lafruit G, Catthoor F (2008). Anisotropic local high-confidence voting for accurate stereo correspondence. Proceedings of the Electronic Imaging 2008. International Society for Optics and Photonics, 2008 Jan 27; San Jose, USA. 68120J.
Ma L, Li J, Ma J, Zhang H (2013a). A modified census transform based on the neighborhood information for stereo matching algorithm. Proceedings of the Seventh International Conference on Image and Graphics (ICIG), 2013 Jul 26-28; Qingdao, China. 533-8.
Ma Z, He K, Wei Y, Sun J, Wu E (2013b). Constant time weighted median filtering for stereo matching and beyond. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013 Dec 1-8; Sydney, Australia. 49-56.
Mei X, Sun X, Dong W, Wang H, Zhang X (2013). Segment-tree based cost aggregation for stereo matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 Jun 23-28; Portland, USA. 313-20.
Menze M, Geiger A (2015). Object scene flow for autonomous vehicles. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 Jun 7-12; Boston, USA. 3061-70.
Michael M, Salmen J, Stallkamp J, Schlipsing M (2013). Real-time stereo vision: Optimizing semi-global matching. Proceedings of the IEEE Intelligent Vehicles Symposium, 2013 Jun 23-26; Gold Coast, Australia. 1197-202.
Min D, Lu J, Do MN (2012). Depth video enhancement based on weighted mode filtering. Pattern Recogn Lett 21:1176-90.
Samadi M, Othman MF (2013). A new fast and robust stereo matching algorithm for robotic systems. Proceedings of the International Conference on Computing and Information Technology, 2013 May 9-10; Bangkok, Thailand. 281-90.
Satoh SI (2011). Simple low-dimensional features approximating NCC-based image matching. Pattern Recogn Lett 32:1902-11.
Scharstein D, Szeliski R (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vision 47:7-42.
Scharstein D, Szeliski R (2015). Middlebury stereo evaluation - Version 2. http://vision.middlebury.edu/stereo/eval/references. Accessed: 2015 May 29.
Schmid K, Tomic T, Ruess F, Hirschmuller H, Suppa M (2013). Stereo vision based indoor/outdoor navigation for flying robots. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013 Nov 3-7; Tokyo, Japan. 3955-62.
Sharma K, Jeong KY, Kim SG (2011). Vision based autonomous vehicle navigation with self-organizing map feature matching technique. Proceedings of the 11th International Conference on Control, Automation and Systems (ICCAS), 2011 Oct 26-29; Gyeonggi-do, South Korea. 946-9.
Tan P, Monasse P (2014). Stereo disparity through cost aggregation with guided filter. Image Processing On Line (IPOL) 4:252-75.
Tippetts BJ, Lee DJ, Archibald JK, Lillywhite KD (2011). Dense disparity real-time stereo vision algorithm for resource-limited systems. IEEE T Circ Syst Vid 21:1547-55.
Vijayanagar KR, Loghman M, Kim J (2013). Real-time refinement of kinect depth maps using multi-resolution anisotropic diffusion. Mobile Netw Appl 19:414-25.
von Gioi RG, Jakubowicz J, Morel JM, Randall G (2012). LSD: a line segment detector. Image Processing On Line (IPOL) 2:35-55.
Wang HQ, Wu M, Zhang YB, Zhang L (2013b). Effective stereo matching using reliable points based graph cut. Proceedings of the Visual Communications and Image Processing (VCIP), 2013 Nov 17-20; Kuching, Malaysia. 1-6.
Wang L, Liu Z, Zhang Z (2014). Feature based stereo matching using two-step expansion. Math Probl Eng 2014:1-14.
Wang YC, Tung CP, Chung PC (2013a). Efficient disparity estimation using hierarchical bilateral disparity structure based graph cut algorithm with a foreground boundary refinement mechanism. IEEE T Circ Syst Vid 23:784-801.
Xiang X, Zhang M, Li G, He Y, Pan Z (2012). Real-time stereo matching based on fast belief propagation. Mach Vision Appl 23:1219-27.
Yang Q (2012a). Recursive bilateral filtering. Proceedings of the 12th European Conference on Computer Vision (ECCV), 2012 Oct 7-13; Florence, Italy. 399-413.
Yang Q (2012b). A non-local cost aggregation method for stereo matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012 Jun 16-21; Rhode Island, USA. 1402-9.
Yang Q, Tan KH, Ahuja N (2009). Real-time O(1) bilateral filtering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009 Jun 20-25; Miami, USA. 557-64.
Yang Q, Ji P, Li D, Yao S, Zhang M (2014). Fast stereo matching using adaptive guided filtering. Image Vision Comput 32:202-11.
Yang R, Pollefeys M (2003). Multi-resolution real-time stereo on commodity graphics hardware. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003 Jun 18-20; Wisconsin, USA. 211-7.
Yoon KJ, Kweon IS (2006). Adaptive support-weight approach for correspondence search. IEEE T Pattern Anal 28:650-6.
Zhang K, Lu J, Yang Q, Lafruit G, Lauwereins R, Van GL (2011). Real-time and accurate stereo: a scalable approach with bitwise fast voting on CUDA, rank transform and disparity calibration. IEEE T Circ Syst Vid 21:867-78.
