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An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images

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Abstract

Object Stereo Vision has conventionally been one of the deeply examined areas in computer vision. Stereo matching is employed in numerous modern applications, including robot navigation, augmented reality, and automotive applications. Even though it has a long research history, it is still challenging for the edges of textureless, discontinues, and occluded regions under radiometric variation. This research article proposes a modified histogram equalization, a novel feature extraction, a spatial gradient model, and matching cost, which is robust and stable to images taken in different radiometric variations. The proposed method reduced the average percentage of bad pixels to 3.35 and reduced the relative mean square error (RMSE) up to 30.08 on the Middlebury dataset for different illumination and exposure values. Quantitative and qualitative evaluation of the proposed method demonstrates significant improvement in increasing PSNR and decreasing bad pixel percentage against radiometric variation and state-of-the-art local stereo matching algorithms.

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References

  • Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 18(9):1921–1935

    Article  MathSciNet  Google Scholar 

  • Arranz A, Sánchez Á, Alvar M (2012) Multiresolution energy minimisation framework for stereo matching. IET Comput Vis 6(5):425–434

    Article  MathSciNet  Google Scholar 

  • Brown MZ, Burschka D, Hager GD (2003) Advances in computational stereo. IEEE Trans Pattern Anal Mach Intell 25(8):993–1008

    Article  Google Scholar 

  • Chang YJ, Ho YS (2016) Disparity map enhancement in pixel based stereo matching method using distance transform. J Vis Commun Image Represent 40:118–127

    Article  Google Scholar 

  • Chondro P, Yao ZR, Ruan SJ (2018) Depth-based dynamic lightness adjustment power-saving algorithm for AMOLED in head-mounted display. Opt Express 26(25):33158–33165

    Article  Google Scholar 

  • Cigla C (2015) Recursive edge-aware filters for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-IEEE), Boston, pp 27–34

  • Cigla C, Alatan AA (2013) Information permeability for stereo matching. Signal Process Image Commun 28(9):1072–1088

    Article  Google Scholar 

  • Comlekciler IT, Gunes S, Irgin C (2017) Three-dimensional repositioning of jaw in the orthognathic surgery using the binocular stereo vision Scientia Iranica

  • De-Maeztu L, Villanueva A, Cabeza R (2011) Stereo matching using gradient similarity and locally adaptive support-weight. Pattern Recognit Lett 32(13):1643–1651

    Article  Google Scholar 

  • Dinh VQ, Pham CC, Jeon JW (2016) Robust adaptive normalized cross-correlation for stereo matching cost computation. IEEE Trans Circuits Syst Video Technol 27(7):1421–1434

    Article  Google Scholar 

  • Hamzah RA, Ibrahim H (2016) Literature survey on stereo vision disparity map algorithms. J Sens

  • Hamzah RA, Ibrahim H, Hassan AHA (2017) Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. J Vis Commun Image Represent 42:45–160

    Article  Google Scholar 

  • Hamzah RA, Kadmin AF, Hamid MS, Ghani SFA, Ibrahim H (2018) Improvement of stereo matching algorithm for 3D surface reconstruction. Signal Process Image Commun 65:165–172

    Article  Google Scholar 

  • Heo YS, Lee KM, Lee SU (2010) Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans Pattern Anal Mach Intell 33(4):807–22

    Google Scholar 

  • Hirschmuller H (2007) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341

    Article  Google Scholar 

  • Hirschmuller H, Scharstein D (2007) Evaluation of cost functions for stereo matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-IEEE) Minneapolis

  • Jiao J, Yang Q, He S, Gu S, Zhang L, Lau RW (2017) Joint image denoising and disparity estimation via stereo structure PCA and noise-tolerant cost. Int J Comput Vis 124(2):204–222

    Article  MathSciNet  Google Scholar 

  • Jung IL, Sim JY, Kim CS, Lee SU (2013) Robust stereo matching under radiometric variations based on cumulative distributions of gradients. In: IEEE International Conference on Image Processing (ICIP-1EEE), pp 2082–2085

  • Kim YH, Koo J, Lee S (2016) Adaptive descriptor-based robust stereo matching under radiometric changes. Pattern Recognit Lett 78:41–47

    Article  Google Scholar 

  • Klaus A, Sormann M, Karner K (2006) Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure. In: proceedings of the International Conference on Pattern Recognition (ICPR-IEEE), Hong Kong, pp 15–18

  • Kordelas GA, Alexiadis DS, Daras P, Izquierdo E (2015) Enhanced disparity estimation in stereo images. Image Vis Comput 35:31–49

    Article  Google Scholar 

  • Lee Z, Juang J, Nguyen TQ (2013) Local disparity estimation with three-moded cross census and advanced support weight. IEEE Trans Multimed 15(8):1855–1864

    Article  Google Scholar 

  • Liang CK, Cheng CC, Lai YC, Chen LG, Chen HH (2011) Hardware-efficient belief propagation. IEEE Trans Circuits Syst Video Technol 21(5):525–537

    Article  Google Scholar 

  • Lim J, Lee S (2018) Patchmatch-based robust stereo matching under radiometric changes. IEEE Trans Pattern Anal Mach Intell 41(5):1203–1212

    Article  Google Scholar 

  • Liu H, Wang R, Xia Y (1869) Zhang X (2020) Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images. Appl Sci 10(5):

  • Ma, Z, He, K, Wei, Y, Sun, J, Wu, E (2013) Constant time weighted median filtering for stereo matching and beyond. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV-IEEE) Sydney, pp 49-56

  • Mei, X, Sun, X, Zhou, M, Jiao, S, Wang, H, Zhang X (2011) On building an accurate stereo matching system on graphics hardware. In: Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV-IEEE), Barcelona, pp 467–474

  • Miled, W, Pesquet-Popescu B (2010) The use of color information in stereo vision processing. In: High-Quality Visual Experience, Springer, Berlin, Heidelberg pp 311-330

  • San TT, War N (2017) Local stereo matching under radiometric variations, 18th IEEE/ACIS International Conference on Software Engineering. Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Kanazawa, pp 245–249

  • Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1):7–42

    Article  Google Scholar 

  • Scharstein D, Pal C (2007) Learning conditional random fields for stereo. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-IEEE) Minneapolis

  • Suhr JK, Jung HG (2014) Dense stereo-based robust vertical road profile estimation using Hough transform and dynamic programming. IEEE Trans Intell Transp Syst 16(3):1528–1536

    Article  Google Scholar 

  • Tsai PC, Chondro P, Ruan SJ (2019) Depth-guided pixel dimming with saliency-oriented power-saving transformation for stereoscope amoled displays. IEEE Trans Circuits Syst Video Technol 30(9):3095–3105

    Article  Google Scholar 

  • Vaish, V, Levoy, M, Szeliski, R, Zitnick, CL, Kang SB (2006) Reconstructing occluded surfaces using synthetic apertures: Stereo, focus and robust measures. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-IEEE) Vol 2, pp 2331–2338

  • Wang H, Ding Z, Lv Z, Wei W, Song H (2016) Local stereo matching based on support weight with motion flow for dynamic scene. IEEE Access 4:4840–4847

    Article  Google Scholar 

  • Yamaguchi K, McAllester D, Urtasun, R (2014) Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: European Conference on Computer Vision, Springer, Cham pp 756–771

  • Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28(4):650–656

    Article  Google Scholar 

  • Zhang, C, Li, Z, Cheng, Y, Cai, R, Chao, H, Rui Y (2015) Meshstereo: A global stereo model with mesh alignment regularization for view interpolation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV-IEEE) pp 2057–2065

  • Zhang X, Chen Z (2013) SAD-based stereo vision machine on a system-on-programmable-chip (SOPC). Sensors 13(3):3014–3027

    Article  MathSciNet  Google Scholar 

  • Zhang K, Lu J, Yang Q, Lafruit G, Lauwereins R, Van Gool L (2011) Real-time and accurate stereo: a scalable approach with bitwise fast voting on CUDA. IEEE Trans Circuits Syst Video Technol 21(7):867–878

    Article  Google Scholar 

  • Zhang Z, Ai X, Canagarajah N, Dahnoun N (2012) Local stereo disparity estimation with novel cost aggregation for sub-pixel accuracy improvement in automotive applications. In: 2012 IEEE Intelligent Vehicles Symposium (IVS-IEEE) Alcala de Henares, pp 99-104

  • Zhu S, Yan L (2016) Local stereo matching algorithm with efficient matching cost and adaptive guided image filter. Vis Comput 33(9):1087–1102

    Article  Google Scholar 

  • Zhu X, Lu H, Yang X, Li Y, Zhang H (2013) Stereo vision based traversable region detection for mobile robots using uv-disparity. In: Proceedings of the 32nd Chinese Control Conference (CCC-IEEE), pp 5785–5790

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Authors and Affiliations

  1. National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan

    Qazi Mazhar ul Haq, Chang Hong Lin & Shanq-Jang Ruan

  2. Head of the Laboratory of Distributed Systems (LSD), National University of Asuncion, San Lorenzo, Paraguay

    Derlis Gregor

Authors
  1. Qazi Mazhar ul Haq
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  2. Chang Hong Lin
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  3. Shanq-Jang Ruan
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  4. Derlis Gregor
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Correspondence to Shanq-Jang Ruan.

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Haq, Q.M.u., Lin, C.H., Ruan, SJ. et al. An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images. J Ambient Intell Human Comput 13, 1953–1967 (2022). https://doi.org/10.1007/s12652-021-02958-8

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  • DOI: https://doi.org/10.1007/s12652-021-02958-8

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