{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:57:56Z","timestamp":1760241476103,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T00:00:00Z","timestamp":1522713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.<\/jats:p>","DOI":"10.3390\/s18041074","type":"journal-article","created":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T13:31:51Z","timestamp":1522762311000},"page":"1074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors"],"prefix":"10.3390","volume":"18","author":[{"given":"Dongchen","family":"Zhu","sequence":"first","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiamao","family":"Li","sequence":"additional","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianshun","family":"Wang","sequence":"additional","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingquan","family":"Peng","sequence":"additional","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Shi","sequence":"additional","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,3]]},"reference":[{"key":"ref_1","unstructured":"Kolmogorov, V., and Zabih, R. (2001, January 7\u201314). Computing visual correspondence with occlusions using graph cuts. Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","unstructured":"Hirschmuller, H. (2005, January 20\u201325). Accurate and efficient stereo processing by semi-global matching and mutual information. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"\u017dbontar, J., and LeCun, Y. (2015, January 7\u201312). Computing the stereo matching cost with a convolutional neural network. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201915), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"ref_5","first-page":"1","article-title":"Stereo matching by training a convolutional neural network to compare image patches","volume":"17","author":"Zbontar","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fabbri, R., and Kimia, B. (2010, January 13\u201318). 3D curve sketch: Flexible curve-based stereo reconstruction and calibration. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539787"},{"key":"ref_7","unstructured":"Scharstein, D., Szeliski, R., and Zabih, R. (2001, January 9\u201310). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), Kauai, HI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TPAMI.2008.221","article-title":"Evaluation of Stereo Matching Costs on Images with Radiometric Differences","volume":"31","author":"Hirschmuller","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., and Zhang, X. (2011, January 6\u201313). On building an accurate stereo matching system on graphics hardware. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130280"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1109\/TCSVT.2009.2020478","article-title":"Cross-Based Local Stereo Matching Using Orthogonal Integral Images","volume":"19","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","first-page":"4199","article-title":"Line Matching in Wide-Baseline Stereo: A Top-Down Approach","volume":"23","author":"Yilmaz","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hofer, M., Donoser, M., and Bischof, H. (2014, January 1\u20135). Semi-Global 3D Line Modeling for Incremental Structure-from-Motion. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.64"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TPAMI.1985.4767639","article-title":"Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming","volume":"PAMI-7","author":"Ohta","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sarkis, M., and Diepold, K. (2008, January 12\u201315). Sparse stereo matching using belief propagation. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4712121"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, W., and Ngan, K.N. (2005, January 13\u201316). Disparity estimation with edge-based matching and interpolation. Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong, China.","DOI":"10.1109\/ISPACS.2005.1595369"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Su, C.Y., Rakheja, S., and Liu, H. (2012). Robust Real-Time Stereo Edge Matching by Confidence-Based Refinement. Intelligent Robotics and Applications, Springer.","DOI":"10.1007\/978-3-642-33503-7"},{"key":"ref_17","unstructured":"Witt, J., and Weltin, U. (2012, January 11\u201315). Sparse stereo by edge-based search using dynamic programming. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mukherjee, S., and Guddeti, R.M.R. (2014, January 22\u201325). A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo vision. Proceedings of the 2014 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India.","DOI":"10.1109\/SPCOM.2014.6983949"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, C.S., Lu, J., and Ma, K.K. (2016, January 20\u201324). Disparity Estimation by Simultaneous Edge Drawing. Proceedings of the Computer Vision\u2014ACCV 2016 Workshops, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54526-4"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., and Torresani, L. (2015, January 7\u201313). High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.65"},{"key":"ref_21","unstructured":"Simonyan, K., and Zisserman, A. (Comput. Sci., 2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, Z., Feng, C., Liu, M.Y., and Ramalingam, S. (2017, January 21\u201326). CASENet: Deep Category-Aware Semantic Edge Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.191"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0262-8856(83)90006-9","article-title":"On the accuracy of the Sobel edge detector","volume":"1","author":"Kittler","year":"1983","journal-title":"Image Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Menze, M., and Geiger, A. (2015, January 7\u201312). Object scene flow for autonomous vehicles. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (July, January 26). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s00138-014-0649-7","article-title":"Multimodal information fusion for urban scene understanding","volume":"27","author":"Xu","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Greveson, E., Shahrokni, A., and Torr, P.H.S. (2013, January 6\u201310). Urban 3D semantic modelling using stereo vision. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630632"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Leonardis, A., Bischof, H., and Pinz, A. (2006, January 7\u201313). SURF: Speeded Up Robust Features. Proceedings of the Computer Vision\u2014ECCV 2006, Graz, Austria.","DOI":"10.1007\/11744085"},{"key":"ref_31","first-page":"147","article-title":"A combined corner and edge detector","volume":"1988","author":"Harris","year":"1988","journal-title":"Proc. Alvey Vis. Conf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2005, January 17\u201321). Fusing points and lines for high performance tracking. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905), Beijing, China.","DOI":"10.1109\/ICCV.2005.104"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Leutenegger, S., Chli, M., and Siegwart, R.Y. (2011, January 6\u201313). BRISK: Binary Robust invariant scalable keypoints. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126542"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bian, J., Lin, W.Y., Matsushita, Y., Yeung, S.K., Nguyen, T.D., and Cheng, M.M. (2017, January 21\u201326). GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.302"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Z., Cheng, Y., Cai, R., Chao, H., and Rui, Y. (2015, January 7\u201313). MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.238"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Geiger, A., Roser, M., and Urtasun, R. (2010, January 8\u201312). Efficient Large-Scale Stereo Matching. Proceedings of the Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand.","DOI":"10.1007\/978-3-642-19315-6_3"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1074\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:28Z","timestamp":1760194768000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,3]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041074"],"URL":"https:\/\/doi.org\/10.3390\/s18041074","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,4,3]]}}}