{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:15:59Z","timestamp":1750306559557,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2014,12,14]],"date-time":"2014-12-14T00:00:00Z","timestamp":1418515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2014,12,14]]},"DOI":"10.1145\/2683483.2683539","type":"proceedings-article","created":{"date-parts":[[2015,5,11]],"date-time":"2015-05-11T16:31:04Z","timestamp":1431361864000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Semantic Motion Segmentation Using Dense CRF Formulation"],"prefix":"10.1145","author":[{"given":"N. Dinesh","family":"Reddy","sequence":"first","affiliation":[{"name":"Robotics Research Center, IIIT Hyderabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prateek","family":"Singhal","sequence":"additional","affiliation":[{"name":"Robotics Research Center, IIIT Hyderabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. Madhava","family":"Krishna","sequence":"additional","affiliation":[{"name":"Robotics Research Center, IIIT Hyderabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,12,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"IEEE International Conference on Robotics and Automation (ICRA)","author":"Wang Chieh-Chih","year":"2014","unstructured":"Chieh-Chih Wang and T. Han Lin . Deep learning of spatio-temporal features with geometric-based moving point detection for motion segmentation . In IEEE International Conference on Robotics and Automation (ICRA) , 2014 . Chieh-Chih Wang and T. Han Lin. Deep learning of spatio-temporal features with geometric-based moving point detection for motion segmentation. In IEEE International Conference on Robotics and Automation (ICRA), 2014."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206547"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.185"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354978"},{"key":"e_1_3_2_1_5_1","first-page":"8","volume-title":"International Conference on Computer Vision (ICCV)","author":"Gould S.","unstructured":"S. Gould , R. Fulton , and D. Koller . Decomposing a scene into geometric and semantically consistent regions . In International Conference on Computer Vision (ICCV) , pages 1\u2013 8 . IEEE, 2009. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In International Conference on Computer Vision (ICCV), pages 1\u20138. IEEE, 2009."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1166"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339615"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.217"},{"volume-title":"Conference on Computer Vision and Pattern Recognition (CVPR) {9}.","author":"Kohli P.","key":"e_1_3_2_1_9_1","unstructured":"P. Kohli , L. Ladicky , and P. H. S. Torr . Robust higher order potentials for enforcing label consistency . In Conference on Computer Vision and Pattern Recognition (CVPR) {9}. P. Kohli, L. Ladicky, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. In Conference on Computer Vision and Pattern Recognition (CVPR) {9}."},{"key":"e_1_3_2_1_10_1","volume-title":"Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning","author":"Koller D.","year":"2009","unstructured":"D. Koller and N. Friedman . Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning . The MIT Press , 2009 . D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press, 2009."},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning (ICML)","volume":"28","year":"2013","unstructured":"Krahenbuhl, Philipp, Koltun, and Vladlen. Parameter learning and convergent inference for dense random fields . In International conference on machine learning (ICML) , volume 28 of JMLR Proceedings, pages 513\u2013521. JMLR.org , 2013 . Krahenbuhl, Philipp, Koltun, and Vladlen. Parameter learning and convergent inference for dense random fields. In International conference on machine learning (ICML), volume 28 of JMLR Proceedings, pages 513\u2013521. JMLR.org, 2013."},{"key":"e_1_3_2_1_12_1","volume-title":"Neural Information Processing Systems(NIPS)","author":"Krahenbuhl P.","year":"2011","unstructured":"P. Krahenbuhl and V. Koltun . Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials . In Neural Information Processing Systems(NIPS) , 2011 . P. Krahenbuhl and V. Koltun. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. In Neural Information Processing Systems(NIPS), 2011."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459248"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/1888150.1888170"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/1888089.1888122"},{"key":"e_1_3_2_1_16_1","first-page":"289","volume-title":"International Conference on Machine Learning ( ICML)","author":"Lafferty J.","unstructured":"J. Lafferty , A. McCallum , and F. Pereira . Conditional random fields: Probabilistic models for segmenting and labeling sequence data . In International Conference on Machine Learning ( ICML) , pages 282\u2013 289 . Morgan Kaufmann, San Francisco, CA, 2001. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning ( ICML), pages 282\u2013289. Morgan Kaufmann, San Francisco, CA, 2001."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2011.5940558"},{"key":"e_1_3_2_1_18_1","first-page":"4099","volume-title":"IEEE International Conference on Robotics and Automation (ICRA) {18}","author":"Namdev R. K.","unstructured":"R. K. Namdev , A. Kundu , K. M. Krishna , and C. V. Jawahar . Motion segmentation of multiple objects from a freely moving monocular camera . In IEEE International Conference on Robotics and Automation (ICRA) {18} , pages 4092\u2013 4099 . R. K. Namdev, A. Kundu, K. M. Krishna, and C. V. Jawahar. Motion segmentation of multiple objects from a freely moving monocular camera. In IEEE International Conference on Robotics and Automation (ICRA) {18}, pages 4092\u20134099."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2013.6629517"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/11744023_1"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33885-4_30"},{"key":"e_1_3_2_1_22_1","volume-title":"British machine vision conference(BMVC)","author":"Torr P.","year":"2010","unstructured":"P. Torr , W. Clocksin , Y. Bastanlar , S. Sengupta , C. Russell , P. Sturgess , and L. Ladicky . Joint optimization for object class segmentation and dense stereo reconstruction . In British machine vision conference(BMVC) , 2010 . P. Torr, W. Clocksin, Y. Bastanlar, S. Sengupta, C. Russell, P. Sturgess, and L. Ladicky. Joint optimization for object class segmentation and dense stereo reconstruction. In British machine vision conference(BMVC), 2010."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03641-5_2"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.175"},{"key":"e_1_3_2_1_25_1","first-page":"709","volume-title":"Conference on Computer Vision and Pattern Recognition (CVPR) {25}","author":"Yao J.","unstructured":"J. Yao , S. Fidler , and R. Urtasun . Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation . In Conference on Computer Vision and Pattern Recognition (CVPR) {25} , pages 702\u2013 709 . J. Yao, S. Fidler, and R. Urtasun. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation. In Conference on Computer Vision and Pattern Recognition (CVPR) {25}, pages 702\u2013709."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.379"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.411"}],"event":{"name":"ICVGIP '14: Indian Conference on Computer Vision Graphics and Image Processing","acronym":"ICVGIP '14","location":"Bangalore India"},"container-title":["Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2683483.2683539","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2683483.2683539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:13:00Z","timestamp":1750227180000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2683483.2683539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,12,14]]},"references-count":27,"alternative-id":["10.1145\/2683483.2683539","10.1145\/2683483"],"URL":"https:\/\/doi.org\/10.1145\/2683483.2683539","relation":{},"subject":[],"published":{"date-parts":[[2014,12,14]]},"assertion":[{"value":"2014-12-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}