{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T06:40:19Z","timestamp":1723704019136},"reference-count":25,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2020,10,1]]},"DOI":"10.1587\/transinf.2020edp7002","type":"journal-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T22:33:03Z","timestamp":1601505183000},"page":"2162-2167","source":"Crossref","is-referenced-by-count":2,"title":["Asymmetric Learning for Stereo Matching Cost Computation"],"prefix":"10.1587","volume":"E103.D","author":[{"given":"Zhongjian","family":"MA","sequence":"first","affiliation":[{"name":"Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Dongzhen","family":"HUANG","sequence":"additional","affiliation":[{"name":"Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences"}]},{"given":"Baoqing","family":"LI","sequence":"additional","affiliation":[{"name":"Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences"}]},{"given":"Xiaobing","family":"YUAN","sequence":"additional","affiliation":[{"name":"Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] M.G. Mozerov and J. van de Weijer, \u201cOne-view occlusion detection for stereo matching with a fully connected CRF model,\u201d IEEE TIP, vol.28, no.6, pp.2936-2947, 2019. 10.1109\/tip.2019.2892668","DOI":"10.1109\/TIP.2019.2892668"},{"key":"2","unstructured":"[2] D. Scharstein, R. Szeliski, and R. Zabih, \u201cA taxonomy and evaluation of dense two-frame stereo correspondence algorithms,\u201d Int. J. Comput. Vis., vol.47, pp.7-42, 2002. 10.1023\/a:1014573219977"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann, \u201cLocal stereo matching using geodesic support weights,\u201d ICIP 2009, pp.2093-2096, 2009. 10.1109\/icip.2009.5414478","DOI":"10.1109\/ICIP.2009.5414478"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] T. Tuytelaars and L. van Gool, \u201cWide baseline stereo matching based on local, affinely invariant regions,\u201d Proc. Brit. Mach. Vis. Conf., pp.38.1-38.14, 2000. 10.5244\/c.14.38","DOI":"10.5244\/C.14.38"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] Y.S. Heo, K.M. Lee, and S.U. Lee, \u201cRobust stereo matching using adaptive normalized cross-correlation,\u201d IEEE Trans. Patt. Anal. Mach. Intell., vol.33, no.4, pp.807-822, 2011. 10.1109\/tpami.2010.136","DOI":"10.1109\/TPAMI.2010.136"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J. Sun, N.-N. Zheng, and H.-Y. Shum, \u201cStereo matching using belief propagation,\u201d IEEE Trans. Patt. Anal. Mach. Intell., vol.25, no.7, pp.787-800, 2003. 10.1109\/tpami.2003.1206509","DOI":"10.1109\/TPAMI.2003.1206509"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Y. Boykov, O. Veksler, and R. Zabih, \u201cFast approximate energy minimization via graph cuts,\u201d IEEE Trans. Patt. Anal. Mach. Intell., vol.23, no.11, pp.1222-1239, 2001. 10.1109\/34.969114","DOI":"10.1109\/34.969114"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] O. Veksler, \u201cStereo correspondence by dynamic programming on a tree,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., vol.2, pp.384-390, 2005. 10.1109\/cvpr.2005.334","DOI":"10.1109\/CVPR.2005.334"},{"key":"9","unstructured":"[9] J. Zbontar and Y. LeCun, \u201cStereo matching by training a convolutional neural network to compare image patches,\u201d JMLR, vol.17, no.1, pp.2287-2318, 2016."},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, \u201cContinuous 3D label stereo matching using local expansion moves,\u201d IEEE Trans. Patt. Anal. Mach. Intell., vol.40, no.11, pp.2725-2739, 2018. 10.1109\/tpami.2017.2766072","DOI":"10.1109\/TPAMI.2017.2766072"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] L. Li, S. Zhang, X. Yu, and L. Zhang, \u201cPMSC: patchmatch-based superpixel cut for accurate stereo matching,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.14, no.8, pp.679-692, 2015. 10.1109\/tcsvt.2016.2628782","DOI":"10.1109\/TCSVT.2016.2628782"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S. Drouyer, S. Beucher, M. Bilodeau, M. Moreaud, and L. Sorbier, \u201cSparse stereo disparity map densification using hierarchical image segmentation,\u201d ISMM, Springer, vol.10225, pp.172-184, 2017. 10.1007\/978-3-319-57240-6_14","DOI":"10.1007\/978-3-319-57240-6_14"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry, \u201cEnd-to-end learning of geometry and context for deep stereo regression,\u201d Proc. IEEE Int. Conf. Comput. Vis. Workshops, pp.66-75, 2017. 10.1109\/iccv.2017.17","DOI":"10.1109\/ICCV.2017.17"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] J.-R. Chang and Y.-S. Chen, \u201cPyramid stereo matching network,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.5410-5418, 2018. 10.1109\/cvpr.2018.00567","DOI":"10.1109\/CVPR.2018.00567"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] M. Bleyer, C. Rhemann, and C. Rother, \u201cPatchMatch stereo: stereo matching with slanted support windows,\u201d Proc. Brit. Mach. Vis. Conf., vol.11, pp.412-425, 2011. 10.5244\/c.25.14","DOI":"10.5244\/C.25.14"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.384-390, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] A. Geiger, P. Lenz, and R. Urtasun, \u201cAre we ready for autonomous driving? The KITTI vision benchmark suite,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.3354-3361, 2012. 10.1109\/cvpr.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] M. Menze and A. Geiger, \u201cObject scene flow for autonomous vehicles,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.3061-3070, 2015. 10.1109\/cvpr.2015.7298925","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] H. Park and K.M. Lee, \u201cLook wider to match image patches with convolutional neural networks,\u201d IEEE Signal Process. Lett., vol.24, no.12, pp.1788-1792, 2017. 10.1109\/lsp.2016.2637355","DOI":"10.1109\/LSP.2016.2637355"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, \u201cRethinking the inception architecture for computer vision,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.2818-2826, 2016. 10.1109\/cvpr.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"21","unstructured":"[21] V. Nair and G. Hinton, \u201cRectified linear units improve restricted Boltzmann machines,\u201d Proc. Int. Conf. Mach. Learn., pp.807-814, 2010."},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] K. Zhang, J. Lu, and G. Lafruit, \u201cCross-based local stereo matching using orthogonal integral images,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.9, no.7, pp.1073-1079, 2009. 10.1109\/tcsvt.2009.2020478","DOI":"10.1109\/TCSVT.2009.2020478"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] H. Hirschmuller, \u201cStereo processing by semi-global matching and mutual information,\u201d IEEE Trans. Patt. Anal. Mach. Intell., vol.30, no.2, pp.328-341, 2008. 10.1109\/tpami.2007.1166","DOI":"10.1109\/TPAMI.2007.1166"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] A. Shaked and L. Wolf, \u201cImproved stereo matching with constant highway networks and reflective confidence learning,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.4641-4650, 2017. 10.1109\/cvpr.2017.730","DOI":"10.1109\/CVPR.2017.730"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] D. Scharstein, H. Hirschm\u00fcller, Y. Kitajima, G. Krathwohl, N. Ne\u0161i\u0107, X. Wang, and P. Westling, \u201cHigh-resolution stereo datasets with subpixel-accurate ground truth,\u201d German Conference on Pattern Recognition, Springer, vol.8753, pp.31-42, 2014. 10.1007\/978-3-319-11752-2_3","DOI":"10.1007\/978-3-319-11752-2_3"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/10\/E103.D_2020EDP7002\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T06:20:43Z","timestamp":1723702843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/10\/E103.D_2020EDP7002\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":25,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2020edp7002","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2020,10,1]]}}}