{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:30Z","timestamp":1750220910397,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T00:00:00Z","timestamp":1578614400000},"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":[[2020,1,10]]},"DOI":"10.1145\/3381271.3381292","type":"proceedings-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T15:59:45Z","timestamp":1582905585000},"page":"163-168","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic bounding-box-labeling method of occluded objects in virtual image data"],"prefix":"10.1145","author":[{"given":"Xinyue","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunication"}]},{"given":"Lingzhong","family":"Meng","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Yunzhi","family":"Xue","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume":"190","author":"Shaohua Q.","unstructured":"Shaohua , Q. , Gongjian , W. and Yaxing , F. 2017. Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model , J. IEEE , 190 9--1925. Shaohua, Q., Gongjian, W. and Yaxing, F. 2017. Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model, J. IEEE, 1909--1925.","journal-title":"J. IEEE"},{"key":"e_1_3_2_1_2_1","volume":"173","author":"Shaohua Q.","unstructured":"Shaohua , Q. , Gongjian , W. and Yaxing , F. 2017. Automatic and Fast PCM Generation for Occluded Object Detection in High-Resolution Remote Sensing Images , J. IEEE , 173 0--1734. Shaohua, Q., Gongjian, W. and Yaxing, F. 2017. Automatic and Fast PCM Generation for Occluded Object Detection in High-Resolution Remote Sensing Images, J. IEEE, 1730--1734.","journal-title":"J. IEEE"},{"key":"e_1_3_2_1_3_1","volume":"215","author":"Guoqing Z.","unstructured":"Guoqing , Z. and Yuefeng , W. 2016. Occlusion detection for urban aerial true orthoimage generation , J. IEEE , 215 3--7003. Guoqing, Z. and Yuefeng, W. 2016. Occlusion detection for urban aerial true orthoimage generation, J. IEEE, 2153--7003.","journal-title":"J. IEEE"},{"key":"e_1_3_2_1_4_1","unstructured":"Zhiqiang H. Chao G. Huaitie X. and Zhuangzhuang T. 2019. Adaptive Weighting Based on Subimage Sparse Model for SAR Occluded Target Recognition J. IEEE 1--13.  Zhiqiang H. Chao G. Huaitie X. and Zhuangzhuang T. 2019. Adaptive Weighting Based on Subimage Sparse Model for SAR Occluded Target Recognition J. IEEE 1--13."},{"key":"e_1_3_2_1_5_1","volume":"222","author":"Henrique C.","unstructured":"Henrique , C. , Mauricio , G. and Aluir , P. 2015. Height-Gradient-Based Method for Occlusion Detection in True Orthophoto Generation , J. IEEE , 222 2--2226. Henrique, C., Mauricio, G. and Aluir, P. 2015. Height-Gradient-Based Method for Occlusion Detection in True Orthophoto Generation, J. IEEE, 2222--2226.","journal-title":"J. IEEE"},{"key":"e_1_3_2_1_6_1","unstructured":"Min Z. Zhengxia Z. Zhenwei S. Wen-Jun Z. and Jie G. 2019. Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images J. IEEE 1--5.  Min Z. Zhengxia Z. Zhenwei S. Wen-Jun Z. and Jie G. 2019. Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images J. IEEE 1--5."},{"key":"e_1_3_2_1_7_1","unstructured":"Ruchan D. Dazhuan X. Jin Z. Licheng J. and Jungang A. 2019.Sig-NMS-Based Faster R-CNN Combining Transfer Learning for Small Target Detection in VHR Optical Remote Sensing Imagery J. IEEE 1--12.  Ruchan D. Dazhuan X. Jin Z. Licheng J. and Jungang A. 2019.Sig-NMS-Based Faster R-CNN Combining Transfer Learning for Small Target Detection in VHR Optical Remote Sensing Imagery J. IEEE 1--12."},{"key":"e_1_3_2_1_8_1","unstructured":"Kaiqiang C. Kun F. Xin G. Menglong Y. Xian S. and Huan Z. 2017. Building extraction from remote sensing images with deep learning in a supervised manner J. IEEE.  Kaiqiang C. Kun F. Xin G. Menglong Y. Xian S. and Huan Z. 2017. Building extraction from remote sensing images with deep learning in a supervised manner J. IEEE."},{"key":"e_1_3_2_1_9_1","unstructured":"Yongtao Y. Tiannan G. Haiyan G. Dilong L. and Shenghua J. 2019. Vehicle Detection from High-Resolution Remote Sensing Imagery Using Convolutional Capsule Networks J. IEEE 1--5.  Yongtao Y. Tiannan G. Haiyan G. Dilong L. and Shenghua J. 2019. Vehicle Detection from High-Resolution Remote Sensing Imagery Using Convolutional Capsule Networks J. IEEE 1--5."},{"key":"e_1_3_2_1_10_1","unstructured":"Yun R. Changren Z. and Shunping X. 2018. Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images J. MDPI AG.  Yun R. Changren Z. and Shunping X. 2018. Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images J. MDPI AG."},{"volume-title":"http:\/\/evaluateai.cn\/#\/detail?id=2","year":"2019","key":"e_1_3_2_1_11_1","unstructured":"SO_data , http:\/\/evaluateai.cn\/#\/detail?id=2 , 2019 -5-10 SO_data, http:\/\/evaluateai.cn\/#\/detail?id=2, 2019-5-10"}],"event":{"name":"ICMIP 2020: 2020 5th International Conference on Multimedia and Image Processing","sponsor":["NJU Nanjing University"],"location":"Nanjing China","acronym":"ICMIP 2020"},"container-title":["Proceedings of the 5th International Conference on Multimedia and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381271.3381292","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3381271.3381292","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:59Z","timestamp":1750203899000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3381271.3381292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,10]]},"references-count":11,"alternative-id":["10.1145\/3381271.3381292","10.1145\/3381271"],"URL":"https:\/\/doi.org\/10.1145\/3381271.3381292","relation":{},"subject":[],"published":{"date-parts":[[2020,1,10]]},"assertion":[{"value":"2020-02-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}