Parallel framework for dense disparity map estimation using Hamming distance

Penza, V., Ortiz, J., Mattos, L.S., Forgione, A., De Momi, E.: Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery. Int. J. Comput. Assist. Radiol. Surg. 11, 197–206 (2016)

Wang, C., Palomar, R., Cheikh, F.A.: Stereo video analysis for instrument tracking in image-guided surgery. In: 2014 5th European Workshop on Visual Information Processing (EUVIP 2014), Paris (2014)

Balicki, M., Sznitman, R., Meisner, E., Taylor, R., Hager, G.: Vision-based proximity detection in retinal surgery. IEEE Trans. Biomed. Eng. 59, 2291–2301 (2012)

Ozgunalp, U., Ai, X., Dahnoun, N.: Stereo vision-based road estimation assisted by efficient planar patch calculation. Signal, Image Video Process. 10, 1127–1134 (2016)

Orfanidis, G., Tefas, A., Nikolaidis, N., Pitas, I.: Signal processing: image communication facial image clustering in stereoscopic videos using double spectral analysis. Signal Process. Image Commun. 33, 86–105 (2015)

Huiltron, V.G., Ponomaryov, V.: Robust approach for disparity map estimation based on multilevel decomposition. IEEE Lat. Am. Trans. 14, 2968–2973 (2016)

El Jaafari, I., El Ansari, M., Koutti, L.: Fast edge-based stereo matching approach for road applications. Signal Image Video Process. 11, 267–274 (2017). doi: 10.1007/s11760-016-0932-3

Ramos-Diaz, E., Kravchenko, V., Ponomaryov, V.: Efficient 2D to 3D video conversion implemented on DSP. EURASIP J. Adv. Signal Process. 2011, 110 (2011)

Gonzalez-Huitron, V., Ramos-Diaz, E., Kravchenko, V., Ponomaryov, V.: 2D to 3D conversion based on disparity map estimation. In: Lecture Notes in Computer Science, vol. 8827, pp. 982–989 (2014)

Kim, C.G.: Parallel SAD for fast dense disparity map using a shared memory programming. In: Park, H.J.J., Jong, Barolli, L., Xhafa, F., Jeong, H.-Y. (eds.) Information technology convergence: security, robotics, automations and communication, pp. 1055–1060. Springer, Netherlands (2013)

Yang, Q., Ji, P., Li, D., Yao, S., Zhang, M.: Fast stereo matching using adaptive guided filtering. Image Vis. Comput. 32, 202–211 (2014)

Zhao, Y., Taubin, G.: Real-time stereo on GPGPU using progressive multi-resolution adaptive windows. Image Vis. Comput. 29, 420–432 (2011)

Ramos-Diaz, E., Gonzalez-Huitron, V., Ponomaryov, V.I., Hernandez-Fragoso, A.: 2D to 3D conversion implemented in different hardware. In: Real-Time Image and Video Processing 2015 (2015)

Makkithaya, U.R.K., Karunakar, A.K.: Anchor-diagonal-based shape adaptive local support region for efficient stereo matching. Signal Image Video Process. 9, 893–901 (2013)

Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Proceedings of European Conference on Computer Vision, pp. 151–158 (1994)

Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Comput. Vis. Image Underst. 114, 1180–1202 (2010)

Zhang, K., Li, J., Li, Y., Hu, W., Sun, L., Yang, S.: Binary stereo matching. In: 2012 21st International Conference on Pattern Recognition (ICPR), Tsukuba, pp. 356–359 (2012)

Kowalczuk, J., Psota, E.T., Prez, L.C.: Real-time Temporal Stereo Matching using Iterative Adaptive Support Weights. In: 2013 IEEE International Conference on Electro/Information Technology (EIT), Rapid City, SD (2013)

Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision (ACCV) (2010)

Einecke, N., Eggert, J.: A two-stage correlation method for stereoscopic depth estimation. In: 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 227–234 (2010)

Hirschmller, H., Innocent, P.R., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. Int. J. Comput. Vis. 47, 229–246 (2002)

Psota, E.T., Kowalczuk, J., Mittek, M., Perez, L.C.: MAP disparity estimation using hidden Markov trees. In: 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, Santiago, pp. 2219–2227 (2015)

Hirschmller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30, 328–341 (2008)

Zhang, Q., Xu, L., Jia, J.: 100+ times faster weighted median filter (WMF). In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2830–2837 (2014)

Wang, Z., Bovik, A.C., Sheikh, H.R., Member, S., Simoncelli, E.P., Member, S.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

Scharstein, D., Hirschmller, H., Kitajima, Y., Krathwohl, G., Nesic, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition (GCPR 2014), Munster (2014)

Hamzah, R.A., Ibrahim, H., Hassan, A.H.A.: Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. J. Vis. Commun. Image Represent. 42, 145–160 (2017). doi: 10.1016/j.jvcir.2016.11.016

Bricola, J.-C., Bilodeau, M., Beucher, S.: A top-down approach to the estimation of depth maps driven by morphological segmentations. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) Mathematical Morphology and Its Applications to Signal and Image Processing: 12th International Symposium, ISMM 2015, Reykjavik, May 27–29, 2015. Proceedings, pp. 122–133. Springer, Cham (2015)