Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications

In the field of stereo vision, some of existing stereo matching algorithms are designed with less accuracy of algorithm. Thus, a new hybrid algorithm with higher accuracy of computation is developed in this project. This thesis will present the design, development and analysis of performance on a de...

Full description

Saved in:

Bibliographic Details
Main Author:	Teo, Chee Huat
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/18177/1/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203D%20Computer%20Vision%20Applications%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18177/2/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203d%20Computer%20Vision%20Applications.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.18177
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abd Manap, Nurulfajar
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Teo, Chee Huat Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
description	In the field of stereo vision, some of existing stereo matching algorithms are designed with less accuracy of algorithm. Thus, a new hybrid algorithm with higher accuracy of computation is developed in this project. This thesis will present the design, development and analysis of performance on a developed Double Stage Filter (DSF) algorithm and other existing stereo matching algorithms. DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. Phase 1 is consists of the part on Sum of Absolute Differences from basic block matching and the part of Scanline Optimization (SO) from dynamic programming approches while phase 2 includes segmentation, merging and basic median filter process. The main feature of DSF algorithm is mainly on the phase 2 or as post-processing in which to remove the unwanted aspects like random noises and horizontal streaks, which is obtained from the raw disparity depth map on the step of optimization. In order to remove the unwanted aspects, two stages filtering process are needed along with the developed approaches in the phase 2 of DSF algorithm. There are two categorized evaluations done on the disparity maps obtained by the algorithms : objective evaluation and subjective evaluation. The objective evaluation includes the evaluation system of Middlebury Stereo Vision website page, computation analysis and traditional methods of Mean Square Errors (MSE), Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Besides, for subjective evaluation, the datasets are captured from LNC IP camera and the results obtained by the selected algorithms are evaluated by human's eyes perception. Based on the results of evaluations, the results obtained by DSF is better than the algorithms, basic block matching and dynamic programming.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Teo, Chee Huat
author_facet	Teo, Chee Huat
author_sort	Teo, Chee Huat
title	Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
title_short	Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
title_full	Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
title_fullStr	Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
title_full_unstemmed	Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications
title_sort	development of double stage filter (dsf) on stereo matching algorithm for 3d computer vision applications
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electronic And Computer Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18177/1/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203D%20Computer%20Vision%20Applications%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18177/2/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203d%20Computer%20Vision%20Applications.pdf
_version_	1747833914900086784
spelling	my-utem-ep.181772021-10-10T14:58:32Z Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications 2016 Teo, Chee Huat T Technology (General) TA Engineering (General). Civil engineering (General) In the field of stereo vision, some of existing stereo matching algorithms are designed with less accuracy of algorithm. Thus, a new hybrid algorithm with higher accuracy of computation is developed in this project. This thesis will present the design, development and analysis of performance on a developed Double Stage Filter (DSF) algorithm and other existing stereo matching algorithms. DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. Phase 1 is consists of the part on Sum of Absolute Differences from basic block matching and the part of Scanline Optimization (SO) from dynamic programming approches while phase 2 includes segmentation, merging and basic median filter process. The main feature of DSF algorithm is mainly on the phase 2 or as post-processing in which to remove the unwanted aspects like random noises and horizontal streaks, which is obtained from the raw disparity depth map on the step of optimization. In order to remove the unwanted aspects, two stages filtering process are needed along with the developed approaches in the phase 2 of DSF algorithm. There are two categorized evaluations done on the disparity maps obtained by the algorithms : objective evaluation and subjective evaluation. The objective evaluation includes the evaluation system of Middlebury Stereo Vision website page, computation analysis and traditional methods of Mean Square Errors (MSE), Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Besides, for subjective evaluation, the datasets are captured from LNC IP camera and the results obtained by the selected algorithms are evaluated by human's eyes perception. Based on the results of evaluations, the results obtained by DSF is better than the algorithms, basic block matching and dynamic programming. 2016 Thesis http://eprints.utem.edu.my/id/eprint/18177/ http://eprints.utem.edu.my/id/eprint/18177/1/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203D%20Computer%20Vision%20Applications%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18177/2/Development%20Of%20Double%20Stage%20Filter%20%28DSF%29%20On%20Stereo%20Matching%20Algorithm%20For%203d%20Computer%20Vision%20Applications.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100100 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electronic And Computer Engineering Abd Manap, Nurulfajar 1. Agrawal, S., 2014. Survey on Image Segmentation Techniques and Color Models. International Journal of Computer Science and Information Technologies, 5(3), pp. 3025–3030. 2. Almeida, J., Leite, N. J., & Torres, R. D. S. , 2012. VISON: VIdeo Summarization for ONline applications. Pattern Recognition Letters, 33(4), pp. 397–409. 3. Amirpour, H., 2013. Predictive Three Step Search ( PTSS ) algorithm for motion estimation. Iranian Conference on Machine Vision and Image Processing (MVIP), 2, pp. 48–52. 4. Anandan, P. , 1989. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2, pp. 283–310. 5. Anantrasirichai, N., Canagarajah, C. N., Redmill, D. W., & Bull, D. R. , 2006. Dynamic Programming for Multi-View Disparity / Depth Estimation Abstract. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 1, pp. 269–272. 6. Benetazzo, a., Fedele, F., Gallego, G., Shih, P. C., & Yezzi, a. , 2012. Offshore stereo measurements of gravity waves. Coastal Engineering, 64, pp. 127–138. 7. Benzeroual, K., Allison, R. S., & Wilcox, L. M. , 2012. 3D display size matters: Compensating for the perceptual effects of S3D display scaling. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 45–52. 8. Bleyer, M., & Gelautz, M. , 2005. A layered stereo matching algorithm using image segmentation and global visibility constraints. ISPRS Journal of Photogrammetry and Remote Sensing, 59(3), pp. 128–150. 9. Bobick, A. F., & Intille, S. S., 1999. Large Occlusion Stereo. International Journal of Computer Vision , 33(3), pp.181–200. 10. Cai, J. , 2007. Fast Stereo Matching : Coarser to Finer with Selective Updating Coarse to Fine Scheme Area-Based Matching. Image and Vision Computing New Zealand, pp. 266–270. 11. Calakli, F., Ulusoy, A. O., Restrepo, M. I., Taubin, G., & Mundy, J. L., 2012. High resolution surface reconstruction from multi-view aerial imagery. Proceedings - 2nd Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2012, pp. 25–32. 12. Carvalho, B. M., Garduno, E., Santos, T. S., Oliveira, L. M., & Silva Neto, J. F., 2013. Fuzzy segmentation of video shots using hybrid color spaces and motion information. Pattern Analysis and Applications, 3, pp. 1–16. 13. Cassisa, C., 2010. Local vs global energy minimization methods: Application to stereo matching. 2010 IEEE International Conference on Progress in Informatics and Computing, 2, pp. 678–683. 14. Chalmond, B., Coldefy, F., Goubet, E., & Lavayssière, B., 2003. Coherent 3-D echo detection for ultrasonic imaging. IEEE Transactions on Signal Processing, 51(3), pp. 592–601. 15. Chen, D., Ardabilian, M., & Chen, L., 2013. A Novel Trilateral Filter based Adaptive Support Weight Method for Stereo Matching. Procedings of the British Machine Vision Conference 2013, pp.1–96. 16. Chen, J., Cai, C., & Li, C., 2012. A Stereo Object Segmentation Algorithm Based on Disparity and Temporal-Spatial Information. International Symposium on Intelligent Signal Processing and Communication Systems, pp. 745–748. 17. Comaniciu, D., Meer, P., & Member, S., 2002. Mean Shift : A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence , 24(5), pp. 603–619. 18. Dell’Agnello, D., Carneiro, G., Chin, T.-J., Castellano, G., & Fanelli, A. M., 2013. Fuzzy clustering based encoding for visual object classification. In Proc. of the 2013 IFSA World Congress - NAFIPS Annual Meeting , pp. 1439–1444. 19. Donate, A., Liu, X., & Collins, E. G., 2011. Efficient path-based stereo matching with subpixel accuracy. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41(1), pp. 183–195. 20. Elboher, E., & Werman, M., 2013. Asymmetric correlation: A noise robust similarity measure for template matching. IEEE Transactions on Image Processing, 22(8), pp. 3062–3073. 21. Fang, L., Li, S., McNabb, R. P., Nie, Q., Kuo, A. N., Toth, C. a., Farsiu, S., 2013. Fast acquisition and reconstruction of optical coherence tomography images via sparse representation. IEEE Transactions on Medical Imaging, 32(11), pp. 2034–2049. 22. Fang, L., Zou, Y., Dong, F., Sun, S., & Lei, B., 2014. Image Thresholding Based on Maximum Mutual Information. International Congress on Image and Signal Processing, pp. 403–409. 23. Felzenszwalb, P. F., & Huttenlocher, D. P., 2000. Efficient matching of pictorial structures. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2, pp. 66–73. 24. Feris, R., Turk, M., & Raskar, R., 2006. Dealing with multi-scale depth changes and motion in depth edge detection. In Brazilian Symposium of Computer Graphic and Image Processing , pp. 3–10. 25. Filho, G. G., & Aloimonos, Y., 2012. An Optimal Time-Space Algorithm for Dense Stereo Matching Gutemberg. Journal of Real-Time Image Processing, pp. 1-15. 26. Fua, P., 1993. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 6(1), pp. 35–49. 27. Geiger, A., Roser, M., & Urtasun, R., 2011. Efficient Large-Scale Stereo Matching. Computer Vision–ACCV, pp. 25–28. 28. Gerace, I., & Pandolfi, R., 2003. A color image restoration with adjacent parallel lines inhibition. Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003, pp. 391–396. 29. Gharib, A., & Harati, A., 2012. Toward Application of Extremal Optimization Algorithm in Image Segmentation, pp. 167–172. 30. Gong, M., & Yang, Y., 2003. Fast stereo matching using reliability-based dynamic programming and consistency constraints. Proceedings Ninth IEEE International Conference on Computer Vision, (Iccv),1, pp. 610–617. 31. Gouiaa, R., & Meunier, J., 2014. 3D Reconstruction by Fusioning Shadow and Silhouette Information. 2014 Canadian Conference on Computer and Robot Vision, pp. 378–384. 32. Guillemaut, J. Y., & Hilton, A., 2012. Space-time joint multi-layer segmentation and depth estimation. Proceedings - 2nd Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2012, pp. 440–447. 33. Hamzah, R. a., Aziz, K. a a, & Shokri, a. S. M., 2012. A pixel to pixel correspondence and region of interest in stereo vision application. IEEE Symposium on Computers and Informatics, ISCI 2012, pp. 193–197. 34. He, X., Zheng, W., Zhao, D., & Du, Y., 2013. Robust Stereo Dense Matching Algorithm Using Similarity Probability. International Journal On Advances in Information Sciences and Service Sciences, 5, pp. 422–430. 35. Heo, Y. S., Lee, K. M., & Lee, S. U., 2011. Robust Stereo matching using adaptive normalized cross-correlation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), pp. 807–822. 36. Hirschmüller, H., 2008. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), pp. 328–341. 37. Hirschmuller, H., & Scharstein, D., 2009. Evaluation of stereo matching costs on images with radiometric differences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), pp. 1582–1599. 38. Hong, P. N., & Ahn, C. W., 2014. Stereo Matching Using Fusion of Spatial Weight Variable Window and Adaptive Support Weight. International Journal of Computer and Electrical Engineering, 6(3), pp. 211–217. 39. Hosni, A., Bleyer, M., & Gelautz, M., 2010. Near real-time stereo with adaptive support weight approaches. International Conference on 3D Vision, pp. 2–6. 40. Hosni, A., Rhemann, C., Bleyer, M., Rother, C., & Gelautz, M., 2013. Fast cost-volume filtering for visual correspondence and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2), pp. 504–511. 41. Hsiao, S., Cheng, J., Wang, W., & Yeh, G., 2012. Rendering Using Hybrid Warping and Hole-Filling. Collision Industry Conference (Cic), pp. 608–611. 42. Hu, T., Wu, T., Song, J., Liu, Q., & Zhang, B., 2011. A New Tree Structure for Weighted Dynamic Programming Based Stereo Algorithm. 2011 Sixth International Conference on Image and Graphics, pp. 100–105. 43. Huang, J., Huang, T.-Z., Zhao, X.-L., Xu, Z.-B., & Lv, X.-G., 2014. Two soft-thresholding based iterative algorithms for image deblurring. Information Sciences, 271, pp. 179–195. 44. Ishikawa, H., 2003. Exact optimization for Markov random fields with convex priors 1 Introduction. IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–17. 45. Jain, P. K., & Susan, S., 2013. An adaptive single seed based region growing algorithm for color image segmentation. 2013 Annual IEEE India Conference, INDICON 2013, pp. 1–5. 46. Jang, W.-S., & Ho, Y.-S., 2014. Discontinuity preserving disparity estimation with occlusion handling. Journal of Visual Communication and Image Representation, 25(7), pp. 1595–1603. 47. Jaspers, H., & Wuensche, H., 2014. Fast and Robust B-Spline Terrain Estimation for Off-Road Navigation with Stereo Vision. IEEE International Conference on Autonomous Robot Systems and Competitions, pp. 140–145. 48. Jodoin, P. M., Mignotte, M., & Rosenberger, C., 2007. Segmentation framework based on label field fusion. IEEE Transactions on Image Processing, 16(10), pp. 2535–2550. 49. Juli, L. F., & Monasse, P., 2015. Bilaterally Weighted Patches for Disparity Map Computation. Image Processing On Line, 5, pp. 73–89. 50. K.T.Chalekar, & T.S.Yengntiwar., 2014. Image Contrast Enhancement By Using Optimal Contrast – tone Mapping Method. International Journal of Advanced Research in Computer Science and Software Engineering, 4(7), pp. 1120–1125. 51. Kanagalakshmi, K., & Chandra, E., 2011. Performance evaluation of filters in noise removal of fingerprint image. ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology, 1, pp. 117–121. 52. Kolmogorov, V., 2006. Convergent tree-reweighted message passing for energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, pp. 1568–1583. 53. Kordelas, G. a., Alexiadis, D. S., Daras, P., & Izquierdo, E., 2015. Enhanced disparity estimation in stereo images. Image and Vision Computing, 35, pp. 31–49. 54. Koschan, a., Rodehorst, V., & Spiller, K., 1996. Color stereo vision using hierarchical block matching and active color illumination. Proceedings of 13th International Conference on Pattern Recognition, 1, pp. 835–839. 55. Kowalczuk, J., Psota, E. T., & Perez, L. C., 2013. Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences. IEEE Transactions on Circuits and Systems for Video Technology, 23(1), pp. 94–104. 56. Lankton, S., Member, S., & Tannenbaum, A., 2008. Localizing Region-Based Active Contours. IEEE Transactions on Image Processing, 17(11), pp. 2029–2039. 57. Lee, S.-Y., Sim, J.-Y., Kim, C.-S., & Lee, S.-U., 2013. Correspondence Matching of Multi-View Video Sequences Using Mutual Information Based Similarity Measure. IEEE Transactions on Multimedia, 15(8), pp. 1719–1731. 58. Lee, Z., Juang, J., & Nguyen, T. Q., 2013. Local disparity estimation with three-moded cross census and advanced support weight. IEEE Transactions on Multimedia, 15(8), pp. 1855–1864. 59. Lee, Z., & Nguyen, T. Q., 2014. Multi-Array Camera Disparity Enhancement. IEEE Transactions on Multimedia, 16(8), pp. 2168–2177. 60. Li, G., 2012. Stereo Matching using Normalized Cross-Correlation in LogRGB Space. Computer Vision in Remote Sensing (CVRS), pp. 19–23. 61. Li, Q., Biswas, M., Pickering, M. R., & Frater, M. R., 2011. Dense depth estimation using adaptive structured light and cooperative algorithm. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 21–28. 62. Li, W., & Li, B., 2008. Virtual view synthesis with heuristic spatial motion. Proceedings - International Conference on Image Processing, ICIP, 1, pp. 1508–1511. 63. Li, Y., Li, H., & Cai, Z., 2014. Fast Orthogonal Haar Transform Pattern Matching via Image Square Sum, 36(9), pp. 1748–1760. 64. Liang, X. Bin, & Wang, J., 2000. A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Transactions on Neural Networks, 11(6), pp. 1251–1262. 65. Lin, M. H., & Tomasi, C. (2004). Surfaces with occlusions from layered stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), pp. 1073–1078. 66. Long, R., Lei, Y., Xiao-Dong, Z., Zuo-Feng, Z., Guang-Sen, L., & Jiaqi-Fei., 2014. An Improved Stereo Match Algorithm Based on Support-Weight Approach. 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, pp. 964–967. 67. Lotfi, N., & Acan, A., 2013. Solving Multiprocessor Scheduling Problem Using Multi-objective Mean Field Annealing, pp. 113–118. 68. Lu, H., Meng, H., Du, K., Sun, Y., Xu, Y., & Zhang, Z., 2014. Post Processing for Dense Stereo Matching by Iterative Local Plane Fitting. IEEE Transactions on Image Processing, pp. 1–5. 69. Luo, C., Lei, J., Hu, G., & Fan, K., 2012. Stereo Matching with Semi-Limited Belief Propagation. Genetic and Evolutionary Computing (ICGEC), pp. 8–11. 70. Ma, Z., & He, K., 2013. Constant Time Weighted Median Filtering for Stereo Matching and Beyond. IEEE International Conference on Computer Vision, pp. 2–9. 71. Malapelle, F., & Fusiello, A., 2013. Uncalibrated Dynamic Stereo Using Parallax. International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 217–222. 72. Malpica, W., & Bovik, A., 2009. Range image quality assessment by structural similarity. Acoustics, Speech and Signal, 1, pp. 1149–1152. 73. Manap, N. A., & Soraghan, J. J., 2012. Disparity Refinement Based on Depth Image Layers Separation for Stereo Matching Algorithms. Journal of Telecommunication, Electronic and Computer Engineering, 4(1), pp. 51-64 . 74. Mattoccia, S., 2009. A locally global approach to stereo correspondence. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1763–1770. 75. Mei, X., Sun, X., Dong, W., Wang, H., & Zhang, X., 2013. Segment-Tree Based Cost Aggregation for Stereo Matching. 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 313–320. 76. Mei, X., Sun, X., Zhou, M., Jiao, S., & Wang, H., 2011. 2nd. On building an accurate stereo matching system on graphics hardware. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 467–474. 77. Mi, X., 2012. Stereo Matching based on Global Edge Constraint and Variable Window Propagation. International Congress on Image and Signal Processing (CISP), pp. 936–940. 78. Michael, M., Salmen, J., Stallkamp, J., & Schlipsing, M., 2013. Real-time Stereo Vision : Optimizing Semi-Global Matching. Intelligent Vehicles Symposium (IV), pp. 1197–1202. 79. Mohammadzade, H., & Hatzinakos, D., 2013. Iterative closest normal point for 3D face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2), pp. 381–397. 80. Mukherjee, S., 2014. A Hybrid Algorithm for Disparity Calculation From Sparse Disparity Estimates Based on Stereo Vision. IEEE International Conference on Computer Vision, pp. 1-6. 81. Mutto, C. D., Zanuttigh, P., Cortelazzo, G. M., & Mattoccia, S., 2011. Scene Segmentation Assisted by Stereo Vision. 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 57–64. 82. Navarro, H., Orghidan, R., Gordan, M., Saavedra, G., & Martinez-Corral, M., 2014. Fuzzy integral imaging camera calibration for real scale 3D reconstructions. IEEE/OSA Journal of Display Technology, 10(7), pp. 601–608. 83. Neilson, D., & Yang, Y. H., 2011. A component-wise analysis of constructible match cost functions for global stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), pp. 2147–2159. 84. Noorshams, N., & Wainwright, M. J., 2013. Stochastic belief propagation: A Low-Complexity Alternative to the Sum-Product Algorithm. IEEE Transactions on Information Theory, 59(4), pp. 1981–2000. 85. Ochs, P., Malik, J., & Brox, T., 2014. Segmentation of moving objects by long term video analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), pp. 1187–1200. 86. Ouyang, W., Tombari, F., Mattoccia, S., Di Stefano, L., & Cham, W. K., 2012. Performance evaluation of full search equivalent Pattern matching algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1), pp. 127–143. 87. Ozenne, V., Toupin, S., Bour, P., Emilien, a., Vaillant, F., de Senneville, B. D., Quesson, B., 2015. Magnetic Resonance Imaging guided cardiac radiofrequency ablation. Iranian Conference on Machine Vision and Image Processing (MVIP), 36(2), pp. 86–91. 88. Panchal, C. S., & Upadhyay, A. B., 2014. Depth Estimation Analysis Using Sum of Absolute Difference Algorithm. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , pp. 6761–6767. 89. Park, C. S., & Park, H. W., 2001. A robust stereo disparity estimation using adaptive window search and dynamic programming search. Pattern Recognition, 34(12), pp. 2573–2576. 90. Pedersoli, M., Vedaldi, A., & Gonzàlez, J., 2011. A coarse-to-fine approach for fast deformable object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 48(5), pp. 1353–1360. 91. Pham, C. C., & Jeon, J. W., 2013. Domain transformation-based efficient cost aggregation for local stereo matching. IEEE Transactions on Circuits and Systems for Video Technology, 23(7), pp. 1119–1130. 92. Pipa, D. R., Da Silva, E. a B., Pagliari, C. L., & Diniz, P. S. R., 2012. Recursive algorithms for bias and gain nonuniformity correction in infrared videos. IEEE Transactions on Image Processing, 21(12), pp. 4758–4769. 93. Proenc, H., Neves, C., & Santos, G., 2013. Segmenting the Periocular Region using a Hierarchical Graphical Model Fed by Texture / Shape Information and Geometrical Constraints. IEEE International Joint Conference on Biometrics Compendium, pp. 1-7. 94. Qin, X., Shen, J., Mao, X., Li, X., & Jia, Y., 2015. Structured-Patch Optimization for Dense Correspondence. IEEE Transactions on Multimedia, 17(3), pp. 295–306. 95. Rana, P. K., Member, S., Taghia, J., Ma, Z., & Flierl, M., 2015. Probabilistic Multiview Depth Image Enhancement Using Variational Inference, 9(3), pp. 435–448. 96. Ray, M. K., 2011. Simplified Novel Method for Edge Detection in Digital Images. International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 197–202. 97. Richardt, C., Orr, D., Davies, I., Criminisi, A., & Dodgson, N. a., 2010. Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6313 LNCS(PART 3), pp. 510–523. 98. Sabater, N., Almansa, A., & Morel, J. M., 2012. Meaningful matches in stereovision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(5), pp. 930–942. 99. Sabatini, M., Monti, R., Gasbarri, P., & Palmerini, G. B., 2013. Adaptive and robust algorithms and tests for visual-based navigation of a space robotic manipulator. Acta Astronautica, 83, pp. 65–84. 100. Savic, V., & Zazo, S., 2010. Nonparametric Belief Propagation Based on Spanning Trees for Cooperative Localization in Wireless Sensor Networks. Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd, pp. 0–4. 101. Scharstein, D., & Hirschm, H., 2014. High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth. Springer International Publishing.,1, pp. 1–12. 102. Scharstein, D., & Szeliski, R., 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), 1, pp. 131–140. 103. Shen, S., & Hu, Z., 2014. How to select good neighboring images in depth-map merging based 3D modeling. IEEE Transactions on Image Processing, 23(1), pp. 308–318. 104. Sinha, S., Scharstein, D., & Szeliski, R., 2013. Efficient High-Resolution Stereo Matching using Local Plane Sweeps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1582–1589. 105. Soroushmehr, S. M. R., Samavi, S., & Shirani, S., 2010. Fast block motion estimation based on sorting of prediction vectors. Canadian Journal of Electrical and Computer Engineering, 35(1), pp. 1-8. 106. Spector, P., 1996. An Introduction to Matlab. Statistical Computing Facility Department of Statistics University of California, Berkeley, pp. 34-40. 107. Stefano, L. Di, Marchionni, M., & Mattoccia, S., 2004. A fast area-based stereo matching algorithm. Image and Vision Computing, 22(12), pp. 983–1005. 108. Strategy, W., Chen, Y., Hung, Y., & Fuh, C., 2001. Fast block matching algorithm based on the winner-update strategy. IEEE Transactions on Image Processing, 10(8), pp. 1212–1222. 109. Su, C., Cormack, L. K., & Bovik, A. C., 2015. Oriented Correlation Models of Distorted Natural Images With Application to Natural Stereopair Quality Evaluation. IEEE Transactions on Image Processing, 24(5), pp. 1685–1699. 110. Sun, X., Mei, X., Jiao, S., Zhou, M., & Wang, H., 2011. 5th. Stereo Matching with Reliable Disparity Propagation. 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 132–139. 111. Sundstr, O., 2009. A Generic Dynamic Programming Matlab Function. IEEE International Conference on Control Applications, 7, pp. 1625–1630. 112. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Rother, C., 2008. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), pp. 1068–1080. 113. Tan, X., Sun, C., Sirault, X., Furbank, R., & Pham, T. D., 2014. Stereo matching using cost volume watershed and region merging. Signal Processing: Image Communication, 29(10), pp. 1–13. 114. Tao, Y., Lin, H., Dong, F., Wang, C., Clapworthy, G., & Bao, H., 2012. Structure-aware lighting design for volume visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), pp. 2372–2381. 115. Thévenaz, P., Ruttimann, U. E., & Unser, M., 1998. A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 7(1), pp. 27–41. 116. Tombari, F., Mattoccia, S., Di Stefano, L., & Addimanda, E., 2008. Classification and evaluation of cost aggregation methods for stereo correspondence. 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. 117. Tombari, F., Stefano, L. Di, Mattoccia, S., Mainetti, A., & Arces, D., 2010. A 3D Reconstruction System Based on Improved Spacetime Stereo. International Conference Control, Automation, Robotics and Vision, pp. 7–10. 118. Torresani, L., Kolmogorov, V., & Rother, C., 2013. A dual decomposition approach to feature correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2), pp. 259–271. 119. Veksler, O., 2005. Stereo Correspondence by Dynamic Programming on a Tree. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(1), pp. 384–390. 120. Vosters, L., & De Haan, G., 2013. Efficient and stable sparse-to-dense conversion for automatic 2-D to 3-D conversion. IEEE Transactions on Circuits and Systems for Video Technology, 23(3), pp. 373–386. 121. Waggoner, J., Zhou, Y., Simmons, J., De Graef, M., & Wang, S., 2014. Graph-cut based interactive segmentation of 3D materials-science images. Machine Vision and Applications, 25(6), pp. 1615–1629. 122. Wang, J., Mei, L., Li, Y., Li, J. Y., Zhao, K., & Yao, Y., 2014). Variable window for outlier detection and impulsive noise recognition in range images. Proceedings - 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014, pp. 857–864. 123. Wang, Y. C., Tung, C. P., & Chung, P. C., 2013. Efficient disparity estimation using hierarchical bilateral disparity structure based graph cut algorithm with a foreground boundary refinement mechanism. IEEE Transactions on Circuits and Systems for Video Technology, 23(5), pp. 784–801. 124. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 13(4), pp. 600–612. 125. Xu, R., Taubman, D., & Naman, A. T., 2014. Nonlinear transform for robust dense block-based motion estimation. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 23(5), pp. 2222–2234. 126. Yang, Q., 2012. A non-local cost aggregation method for stereo matching. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 1402–1409. 127. Yang, Q., Engels, C., & Akbarzadeh, A., 2008. Near real-time stereo for weakly-textured scenes. Proceedings of the British Machine Vision Conference, 72, pp. 1–72. 128. Yang, Q., Ji, P., Li, D., Yao, S., & Zhang, M., 2014. Fast stereo matching using adaptive guided filtering. Image and Vision Computing, 32(3), pp. 202–211. 129. Yang, Y., Wang, X., & Liang, Q., 2014. Belief Propagation Stereo Matching Algorithm Based On Ground Control Points and Spatiotemporal Consistency. International Congress on Image and Signal Processing Belief, pp. 78–82. 130. Yin, Y., Jin, M., & Yi Xie, S., 2010. A stereo pairs disparity matching algorithm by mean-shift segmentation. 3rd International Workshop on Advanced Computational Intelligence, IWACI 2010, pp. 639–642. 131. Youlian, Z., Cheng, H., & Lifang, Z., 2013. A Median Image Filtering Algorithm Based on Statistical Histogram. 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation, 1, pp. 17–20. 132. Yuanhui, Y., Haiying, X., Siqi, H., & Wenjing, X., 2014. An improved matching algorithm for feature points matching. Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-5. 133. Zhang, K., Lu, J., & Lafruit, G., 2009. Cross-based local stereo matching using orthogonal integral images. IEEE Transactions on Circuits and Systems for Video Technology, 19(7), pp. 1073–1079. 134. Zhang, S., Liu, S., Mao, Y., & Wang, X., 2012. Global optimization for bidirectional stereo matching with occlusion handling. Proceedings of 2012 International Conference on Measurement, Information and Control, MIC 2012, 2, pp. 553–557. 135. Zhao, J., & Katupitiya, J., 2007. A multi-window stereo vision algorithm with improved performance at object borders. Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, pp. 66–71. 136. Zheng, E., Dunn, E., Jojic, V., & Frahm, J., 2013. PatchMatch Based Joint View Selection and Depthmap Estimation. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1510-1517. 137. Zhou, C., Troccoli, A., & Pulli, K., 2012. Robust stereo with flash and no-flash image pairs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 342–349. 138. Zhou, Z., Han, T. X., & He, Z., 2012. Semi-supervised learning for robust car windshield tracking and monitoring in live traffic videos. Proceedings - International Conference on Image Processing, ICIP, pp. 489–492. 139. Zhu, L., Chen, Y., Lin, Y., Lin, C., & Yuille, A., 2009. Recursive Segmentation and Recognition Templates for 2D Parsing. Advances in Neural Information Processing Systems 21, 34(2), pp. 1985–1992.

Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications

Similar Items