{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:14Z","timestamp":1777704194874,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p>In recent years, traditional machine learning algorithms have been gradually replaced by deep learning algorithms. In the field of computer vision, convolutional neural network is considered to be the most successful deep learning model. Based on convolutional neural network, the accuracy of image classification has been greatly improved. In this paper, a method for semantic image segmentation based on convolutional neural network is proposed. Firstly, the disparity map is introduced to improve the segmentation accuracy. To obtain the disparity map with more continuous disparity values, an image smoothing method is used to optimize the disparity map. Then, based on the AlexNet network, a fully convolutional network architecture is proposed for semantic image segmentation. The unpooling operation is employed to restore the extracted features to their original sizes. The experimental results demonstrate that the network can achieve high pixel-wise prediction accuracy and that using RGB-D image as the input of the network can reduce the noisy segmentation outputs.<\/jats:p>","DOI":"10.3233\/jifs-162254","type":"journal-article","created":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T12:28:25Z","timestamp":1512131305000},"page":"3397-3404","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Study on semantic image segmentation based on convolutional neural network"],"prefix":"10.1177","volume":"33","author":[{"given":"Lin-Hui","family":"Li","sequence":"first","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Na","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Fu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"443","article-title":"Support vector machines (SVM) for color image segmentation with applications to mobile robot localization problems","author":"Zou A.M.","year":"2005","unstructured":"ZouA.M., HouZ.G. and TanM., Support vector machines (SVM) for color image segmentation with applications to mobile robot localization problems, International Conference on Advances in Intelligent Computing, 2005, pp. 443\u2013452.","journal-title":"International Conference on Advances in Intelligent Computing"},{"issue":"3","key":"e_1_3_2_3_2","first-page":"1","article-title":"Decision tree classification","volume":"39","author":"Zhang G.J.","year":"2008","unstructured":"ZhangG.J. and WangS., Decision tree classification, Jilin Normal University Journal39(3) (2008), 1\u20131.","journal-title":"Jilin Normal University Journal"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/14498596.2010.487851"},{"key":"e_1_3_2_5_2","volume-title":"Applying HOG feature to the detection and tracking of a human on a bicycle","author":"Jung H.","year":"2011","unstructured":"JungH., TanJ.K., IshikawaS. and MorieT., Applying HOG feature to the detection and tracking of a human on a bicycle, International Conference on Control, Automation and Systems, 2011, pp. 1740\u20131743."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(90)90017-F"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"issue":"2","key":"e_1_3_2_9_2","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","unstructured":"KrizhevskyA., SutskeverI. and HintonG.E., ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems25(2) (2012), 1\u20139.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"e_1_3_2_11_2","unstructured":"ShelhamerE. LongJ. and DarrellT. Fully convolutional networks for semantic segmentation 79(10) (2014) 1337\u20131342."},{"issue":"4","key":"e_1_3_2_12_2","first-page":"357","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","volume":"2014","author":"Chen L.","year":"2014","unstructured":"ChenL., PapandreouG. and KokkinosI., Semantic image segmentation with deep convolutional nets and fully connected CRFs, Computer Science2014(4) (2014), 357\u2013361.","journal-title":"Computer Science"},{"issue":"99","key":"e_1_3_2_13_2","first-page":"1","article-title":"Exploring context with deep structured models for semantic segmentation","author":"Lin G.","year":"2017","unstructured":"LinG., ShenC., HengelA.V.D. and ReidI., Exploring context with deep structured models for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine IntelligencePP(99) (2017), 1\u20131.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11206-0_9"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2366600"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-007-0090-8"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"e_1_3_2_20_2","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe S.","year":"2015","unstructured":"IoffeS. and SzegedyC., Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, 2015, pp. 448\u2013456."},{"key":"e_1_3_2_21_2","volume-title":"Delving deep into rectifiers: Surpassing human-level performance on imageNet classification","author":"He K.","year":"2015","unstructured":"HeK., ZhangX., RenS. and SunJ., Delving deep into rectifiers: Surpassing human-level performance on imageNet classification, Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026\u20131034."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.304"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_3_2_25_2","article-title":"SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling","author":"Badrinarayanan V.","year":"2015","unstructured":"BadrinarayananV., HandaA. and CipollaR., SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, Computer Science (2015).","journal-title":"Computer Science"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-162254","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-162254","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-162254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:41Z","timestamp":1777455641000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-162254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":24,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["10.3233\/JIFS-162254"],"URL":"https:\/\/doi.org\/10.3233\/jifs-162254","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12]]}}}