{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:37:59Z","timestamp":1743057479128,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811611025"},{"type":"electronic","value":"9789811611032"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-1103-2_4","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T08:03:32Z","timestamp":1616659412000},"page":"34-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep over and Under Exposed Region Detection"],"prefix":"10.1007","author":[{"given":"Darshita","family":"Jain","sequence":"first","affiliation":[]},{"given":"Shanmuganathan","family":"Raman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"4_CR1","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: ACM SIGGRAPH 2008 Classes, pp. 1\u201310 (2008)","DOI":"10.1145\/1401132.1401174"},{"issue":"8","key":"4_CR3","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.1109\/JSEN.2017.2668378","volume":"17","author":"DJ Griffiths","year":"2017","unstructured":"Griffiths, D.J., Wicks, A.: High speed high dynamic range video. IEEE Sens. J. 17(8), 2472\u20132480 (2017). https:\/\/doi.org\/10.1109\/JSEN.2017.2668378","journal-title":"IEEE Sens. J."},{"issue":"12","key":"4_CR4","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"4_CR5","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1109\/TPAMI.2004.88","volume":"26","author":"MD Grossberg","year":"2004","unstructured":"Grossberg, M.D., Nayar, S.K.: Modeling the space of camera response functions. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1272\u20131282 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Wang, L., Wei, L.-Y., Zhou, K., Guo, B., Shum, H.-Y.: High dynamic range image hallucination. In: Rendering Techniques, pp. 321\u2013326 (2007)","DOI":"10.1145\/1278780.1278867"},{"issue":"4","key":"4_CR7","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1137\/120888302","volume":"6","author":"L Hou","year":"2013","unstructured":"Hou, L., Ji, H., Shen, Z.: Recovering over-\/underexposed regions in photographs. SIAM J. Imaging Sci. 6(4), 2213\u20132235 (2013)","journal-title":"SIAM J. Imaging Sci."},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Guo, D., Cheng, Y., Zhuo, S., Sim, T.: Correcting over-exposure in photographs. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 515\u2013521. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540170"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Salscheider, N.O.: Simultaneous object detection and semantic segmentation. arXiv preprint arXiv:1905.02285 (2019)","DOI":"10.5220\/0009142905550561"},{"key":"4_CR10","first-page":"1","volume":"54","author":"SA Taghanaki","year":"2020","unstructured":"Taghanaki, S.A., Abhishek, K., Cohen, J.P., Cohen-Adad, J., Hamarneh, G.: Deep semantic segmentation of natural and medical images: a review. Artif. Intell. Rev. 54, 1\u201342 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"4_CR11","unstructured":"Schmitt, M., Prexl, J., Ebel, P., Liebel, L., Zhu, X.X.: Weakly supervised semantic segmentation of satellite images for land cover mapping-challenges and opportunities. arXiv preprint arXiv:2002.08254 (2020)"},{"issue":"10","key":"4_CR12","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"4_CR13","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TVCG.2010.63","volume":"17","author":"X Di","year":"2010","unstructured":"Di, X., Doutre, C., Nasiopoulos, P.: Correction of clipped pixels in color images. IEEE Trans. Visual Comput. Graph. 17(3), 333\u2013344 (2010)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Rouf, M., Lau, C., Heidrich, W.: Gradient domain color restoration of clipped highlights. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 7\u201314. IEEE (2012)","DOI":"10.1109\/CVPRW.2012.6239193"},{"issue":"9","key":"4_CR15","doi-asserted-by":"publisher","first-page":"17159","DOI":"10.3390\/s140917159","volume":"14","author":"Y-J Yoon","year":"2014","unstructured":"Yoon, Y.-J., Byun, K.-Y., Lee, D.-H., Jung, S.-W., Ko, S.-J.: A new human perception-based over-exposure detection method for color images. Sensors 14(9), 17159\u201317173 (2014)","journal-title":"Sensors"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Marnerides, D., Bashford-Rogers, T., Hatchett, J., Debattista, K.: ExpandNet: a deep convolutional neural network for high dynamic range expansion from low dynamic range content. In: Computer Graphics Forum, vol. 37, pp. 37\u201349. Wiley Online Library (2018)","DOI":"10.1111\/cgf.13340"},{"issue":"10","key":"4_CR17","doi-asserted-by":"publisher","first-page":"182-1","DOI":"10.2352\/ISSN.2470-1173.2020.10.IPAS-181","volume":"2020","author":"Z Gao","year":"2020","unstructured":"Gao, Z., Edirisinghe, E., Chesnokov, S.: OEC-CNN: a simple method for over-exposure correction in photographs. Electron. Imaging 2020(10), 182-1 (2020)","journal-title":"Electron. Imaging"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"4_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"4_CR20","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)"},{"issue":"4","key":"4_CR21","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence 40(4), 834\u2013848 (2017)","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Switchable temporal propagation network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 87\u2013102 (2018)","DOI":"10.1007\/978-3-030-01234-2_6"},{"issue":"1\u20133","key":"4_CR24","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"BC Russell","year":"2008","unstructured":"Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1\u20133), 157\u2013173 (2008)","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"4_CR25","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Marcel, S., Rodriguez, Y.: Torchvision the machine-vision package of torch. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1485\u20131488 (2010)","DOI":"10.1145\/1873951.1874254"},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/978-1-4842-2766-4_12","volume-title":"Deep Learning with Python","author":"N Ketkar","year":"2017","unstructured":"Ketkar, N.: Introduction to PyTorch. In: Ketkar, N. (ed.) Deep Learning with Python, pp. 195\u2013208. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-1-4842-2766-4_12"},{"key":"4_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"2","key":"4_CR29","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"4_CR30","unstructured":"Minhas, M.S.: Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3. GitHub.com\/msminhas93, 12 September 2019. https:\/\/github.com\/msminhas93\/DeepLabv3FineTuning"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1103-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T20:16:01Z","timestamp":1724703361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1103-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811611025","9789811611032"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1103-2_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prayagraj","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2020.iiita.ac.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"352","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was partially held in a virtual mode.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}