{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:43:11Z","timestamp":1743111791028,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819785018"},{"type":"electronic","value":"9789819785025"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8502-5_11","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"144-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning 360\u00b0 Optical Flow Using Tangent Images and Transformer"],"prefix":"10.1007","author":[{"given":"Yanjie","family":"Ma","sequence":"first","affiliation":[]},{"given":"Cheng","family":"Han","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Xv","sequence":"additional","affiliation":[]},{"given":"Wudi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Baohua","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Phan, T.-B., Trinh, D.-H. Lamarque, D., et al.: Dense optical flow for the reconstruction of weakly textured and structured surfaces: application to endoscopy. In: 2019 IEEE International Conference on Image Processing, pp. 310\u20133142. Taipei (2019)","DOI":"10.1109\/ICIP.2019.8802948"},{"issue":"06","key":"11_CR2","first-page":"1901","volume":"39","author":"Y Lin","year":"2022","unstructured":"Lin, Y., Zhou, W.: Deep learning-based algorithm for generating edge information of optical flow frame interpolation. Comput. Appl. Res. 39(06), 1901\u20131904 (2022)","journal-title":"Comput. Appl. Res."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Lup, V., Nedevschi, S.: Video semantic segmentation leveraging dense optical flow. In: 16th International Conference on Intelligent Computer Communication and Processing, pp. 369\u2013376. Cluj-Napoca, Romania (2020)","DOI":"10.1109\/ICCP51029.2020.9266150"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Dong, Y.: Faint moving small target detection based on optical flow method. In: 7th International Conference on Intelligent Computing and Signal Processing, pp. 391\u2013395. Xi'an, China (2022)","DOI":"10.1109\/ICSP54964.2022.9778780"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhao, B., Zhang, D.: The elder care robot based on panoramic vision. In: 2022 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE), pp. 266\u2013271. Chiang Mai, Thailand (2022)","DOI":"10.1109\/ISEEIE55684.2022.00054"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Akdemir, B., Belbachi, A.-M., Svendsen, L.-M.: Real-time vehicle localization and tracking using monocular panomorph panoramic vision. In: 24th International Conference on Pattern Recognition (ICPR), pp. 2350\u20132355. China, Beijing (2018)","DOI":"10.1109\/ICPR.2018.8546104"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"17880","DOI":"10.1109\/ACCESS.2018.2820326","volume":"6","author":"L Meng","year":"2018","unstructured":"Meng, L., Hirayama, T., Oyanagi, S.: Underwater-drone with panoramic camera for automatic fish recognition based on deep learning. IEEE Access 6, 17880\u201317886 (2018)","journal-title":"IEEE Access"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Xue, C., Zhang, J., Hao, Y.: Research on distortion algorithm of panoramic image unfolding map. In: 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), pp. 98\u2013102. Guangzhou, China (2022)","DOI":"10.1109\/AHPCAI57455.2022.10087441"},{"key":"11_CR9","unstructured":"Su, Y.C., Grauman. K.: Learning spherical convolution for fast features from 360\u00b0 imagery. In: European Conference on Computer Vision, pp. 525\u2013541 (2018)"},{"issue":"2","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/LRA.2020.2967274","volume":"5","author":"LC Fernandez","year":"2020","unstructured":"Fernandez, L.C., Facil, J., Perez, Y.A., et al.: Corners for layout: end-to-end layout recovery from 360 images. IEEE Robot. Autom. Lett. 5(2), 1255\u20131262 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Ling, Z., Xing, Z., et al.: PanoSwin: a Pano-style swin transformer for panorama understanding. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17755\u201317764. Canada (2023)","DOI":"10.1109\/CVPR52729.2023.01703"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Bhandari, K., Zong, Z., Yan, Y.: LiteFlowNet360: revisiting optical flow estimation in 360 videos. In: 25th International Conference on Pattern Recognition, pp. 8196\u20138203. Milan, Italy (2021)","DOI":"10.1109\/ICPR48806.2021.9412035"},{"key":"11_CR13","unstructured":"Yuan, M., Richardt, C.: 360\u00b0 optical flow using tangent images. In: 32th International Proceedings of the British Machine Vision Conference (2021)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y., Barnes, C., Huang, K., et al.: Deep 360\u00b0 optical flow estimation based on multi-projection fusion. In: Computer Vision\u2013ECCV 17th European Conference, pp. 336\u2013352. Tel Aviv, Israel (2022)","DOI":"10.1007\/978-3-031-19833-5_20"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Yuan, L., et al.: Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: 2021 IEEE\/CVF International Conference on Computer Vision, pp. 538\u2013547. Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Bhandari, K., Duan, B., Liu, G., et al.: Learning omnidirectional flow in 360\u00b0 video via Siamese. In: 17th European Conference. Representation. Computer Vision, pp. 557\u2013574. Tel Aviv, Israel (2022)","DOI":"10.1007\/978-3-031-20074-8_32"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"69989","DOI":"10.1109\/ACCESS.2023.3290993","volume":"11","author":"E Kim","year":"2023","unstructured":"Kim, E., Jun, W., Heo, J.-P.: Axial constraints for global matching-based optical flow estimation. IEEE Access 11, 69989\u201370000 (2023)","journal-title":"IEEE Access"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Newsam, S.: Densenet for dense flow. In: 2017 IEEE International Conference on Image Processing, pp. 790\u2013794 (2017)","DOI":"10.1109\/ICIP.2017.8296389"},{"key":"11_CR19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., et al.: Attention is all you need. arXiv. In NeurIPS, pp. 5998\u20136008 (2017)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Coors, B., Condurache, A.-P., Geiger, A.: SphereNet: learning spherical representations for detection and classification in omnidirectional images. In: 14th Proceedings of the IEEE Conference on European Conference and Computer Vision, pp. 518\u2013533 (2018)","DOI":"10.1007\/978-3-030-01240-3_32"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Eder, M., Shvets, M., et al.: Tangent images for mitigating spherical distortion. In: 25th Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01244"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Guo, Y., Yan, Z., Huang, X., Duan, Y., Ren, L.: OmniFusion: 360 monocular depth estimation via geometry-aware fusion. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2791\u20132800. New Orleans, LA, USA (2022)","DOI":"10.1109\/CVPR52688.2022.00282"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Xu, C.: Applying MLP and CNN on handwriting images for image classification task. In: 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, pp. 830\u2013835. Wuhan, China (2022)","DOI":"10.1109\/AEMCSE55572.2022.00167"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Xu, H., Zhang, J., Cai, J., et al.: Gmflow: learning optical flow via global matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8121\u20138130. New Orleans, LA, USA (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00795","DOI":"10.1109\/CVPR52688.2022.00795"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Li, Z.-H., Liu, X.-T., Drenkow, N., et al.: Revisiting stereo depth estimation from a sequence perspective with transformers. In: 2021 IEEE\/CVF International Conference on Computer Vision, pp. 6197\u20136206. Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00614"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Khan, I.U., Han, K., Lee, J.W.: TransUser's: a transformer based salient object detection for users experience generation in 360\u00b0 videos. In: 2024 IEEE International Conference on Artificial Intelligence and extended and Virtual Reality, pp. 256\u2013260. Los Angeles, USA (2024)","DOI":"10.1109\/AIxVR59861.2024.00042"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Sun, J.-M., Shen, Z.-H., Wang, Y., et al.: Loftr: detector-free local feature matching with transformers. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8922\u20138931. Nashville, TN, USA (2021)","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TMM.2023.3271816","volume":"26","author":"R Liu","year":"2024","unstructured":"Liu, R., Cheng, Y., Huang, S., Li, C., Cheng, X.: Transformer-based high-fidelity facial displacement completion for detailed 3D face reconstruction. IEEE Trans. Multimedia 26, 799\u2013810 (2024). https:\/\/doi.org\/10.1109\/TMM.2023.3271816","journal-title":"IEEE Trans. Multimedia"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Shi, H., Zhou, Y., Yang, K., et al.: Csflow: learning optical flow via cross strip correlation for autonomous driving (2022)","DOI":"10.1109\/IV51971.2022.9827341"},{"issue":"5","key":"11_CR30","doi-asserted-by":"publisher","first-page":"5570","DOI":"10.1109\/TITS.2023.3241212","volume":"24","author":"H Shi","year":"2023","unstructured":"Shi, H., Zhou, Y., Yang, K., et al.: PanoFlow: learning optical flow for panoramic images. IEEE Trans. Intell. Transp. Syst. 24(5), 5570\u20135585 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Fischer, Ilg, E., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766. (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 21th Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040\u20134048 (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"11_CR33","doi-asserted-by":"publisher","unstructured":"Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: 16th Proceedings of the IEEE Conference on European Conference and Computer Vision, pp. 402\u2013419 (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Artizzu, C.-O., Zhang, H., Allibert, G., Demonceaux, C.: OmniFlowNet: a perspective neural network adaptation for optical flow estimation in omnidirectional images. In: 26th Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2657\u20132662 (2021)","DOI":"10.1109\/ICPR48806.2021.9412745"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8502-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:17:47Z","timestamp":1730384267000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8502-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819785018","9789819785025"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8502-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}