{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:17:05Z","timestamp":1743149825696,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819786848"},{"type":"electronic","value":"9789819786855"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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\/978-981-97-8685-5_25","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:07:02Z","timestamp":1730524022000},"page":"354-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Redundancy-Suppression Based Event Sampling Method for Structured Representation"],"prefix":"10.1007","author":[{"given":"Jupo","family":"Ma","sequence":"first","affiliation":[]},{"given":"Shunhong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Alzugaray, I., Chli, M.: Ace: an efficient asynchronous corner tracker for event cameras. In: 2018 International Conference on 3D Vision (3DV), pp. 653\u2013661 (2018)","DOI":"10.1109\/3DV.2018.00080"},{"issue":"2","key":"25_CR2","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1109\/TNNLS.2013.2273537","volume":"25","author":"R Benosman","year":"2014","unstructured":"Benosman, R., Clercq, C., Lagorce, X., Ieng, S.H., Bartolozzi, C.: Event-based visual flow. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 407\u2013417 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Cen, M., Jung, C.: Fully convolutional siamese fusion networks for object tracking. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3718\u20133722 (2018)","DOI":"10.1109\/ICIP.2018.8451102"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Chen, N.F.Y.: Pseudo-labels for supervised learning on dynamic vision sensor data, applied to object detection under ego-motion. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 757\u201375709 (2018)","DOI":"10.1109\/CVPRW.2018.00107"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Cladera, F., Bisulco, A., Kepple, D., Isler, V., Lee, D.D.: On-device event filtering with binary neural networks for pedestrian detection using neuromorphic vision sensors. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 3084\u20133088 (2020)","DOI":"10.1109\/ICIP40778.2020.9191148"},{"issue":"3","key":"25_CR6","doi-asserted-by":"publisher","first-page":"4596","DOI":"10.1109\/LRA.2020.3002480","volume":"5","author":"Y Deng","year":"2020","unstructured":"Deng, Y., Li, Y., Chen, H.: AMAE: adaptive motion-agnostic encoder for event-based object classification. IEEE Robot. Autom. Lett. 5(3), 4596\u20134603 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Duan, P., Wang, Z.W., Zhou, X., Ma, Y., Shi, B.: Eventzoom: learning to denoise and super resolve neuromorphic events. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12819\u201312828 (2021)","DOI":"10.1109\/CVPR46437.2021.01263"},{"issue":"1","key":"25_CR8","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","volume":"44","author":"G Gallego","year":"2022","unstructured":"Gallego, G., Delbr\u00fcck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A.J., Conradt, J., Daniilidis, K., Scaramuzza, D.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 154\u2013180 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Gallego, G., Rebecq, H., Scaramuzza, D.: A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3867\u20133876 (2018)","DOI":"10.1109\/CVPR.2018.00407"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Gehrig, D., Loquercio, A., Derpanis, K., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5632\u20135642 (2019)","DOI":"10.1109\/ICCV.2019.00573"},{"key":"25_CR11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/978-3-030-58517-4_6","volume-title":"Computer Vision\u2014ECCV 2020","author":"Y Hu","year":"2020","unstructured":"Hu, Y., Delbruck, T., Liu, S.C.: Learning to exploit multiple vision modalities by using grafted networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision\u2014ECCV 2020, pp. 85\u2013101. Springer International Publishing, Cham (2020)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Iacono, M., Weber, S., Glover, A., Bartolozzi, C.: Towards event-driven object detection with off-the-shelf deep learning. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.\u00a01\u20139 (2018)","DOI":"10.1109\/IROS.2018.8594119"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, B., Li, Z., Asif, M.S., Cao, X., Ma, Z.: Token-based spatiotemporal representation of the events. In: ICASSP 2024\u20142024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5240\u20135244 (2024)","DOI":"10.1109\/ICASSP48485.2024.10447951"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Kim, J., Bae, J., Park, G., Zhang, D., Kim, Y.M.: N-imagenet: Towards robust, fine-grained object recognition with event cameras. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2126\u20132136 (2021)","DOI":"10.1109\/ICCV48922.2021.00215"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Kogler, J., Sulzbachner, C., Kubinger, W.: Bio-inspired stereo vision system with silicon retina imagers. In: Computer Vision Systems, pp. 174\u2013183. Berlin, Heidelberg (2009)","DOI":"10.1007\/978-3-642-04667-4_18"},{"issue":"11","key":"25_CR16","doi-asserted-by":"publisher","first-page":"14020","DOI":"10.1109\/TPAMI.2023.3298925","volume":"45","author":"D Li","year":"2023","unstructured":"Li, D., Tian, Y., Li, J.: Sodformer: streaming object detection with transformer using events and frames. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 14020\u201314037 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR17","doi-asserted-by":"publisher","first-page":"2975","DOI":"10.1109\/TIP.2022.3162962","volume":"31","author":"J Li","year":"2022","unstructured":"Li, J., Li, J., Zhu, L., Xiang, X., Huang, T., Tian, Y.: Asynchronous spatio-temporal memory network for continuous event-based object detection. IEEE Trans. Image Process. 31, 2975\u20132987 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR18","unstructured":"Neil, D., Pfeiffer, M., Liu, S.C.: Phased LSTM: accelerating recurrent network training for long or event-based sequences. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3889\u20133897. Red Hook, NY, USA (2016)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Do, T.T., Caldwell, D.G., Tsagarakis, N.G.: Real-time 6dof pose relocalization for event cameras with stacked spatial LSTM networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1638\u20131645 (2019)","DOI":"10.1109\/CVPRW.2019.00207"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Rebecq, H., Horstschaefer, T., Scaramuzza, D.: Real-time visual-inertial odometry for event cameras using keyframe-based nonlinear optimization. In: British Machine Vision Conference (2017)","DOI":"10.5244\/C.31.16"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: Events-to-video: bringing modern computer vision to event cameras. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3852\u20133861 (2019)","DOI":"10.1109\/CVPR.2019.00398"},{"issue":"6","key":"25_CR22","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TPAMI.2019.2963386","volume":"43","author":"H Rebecq","year":"2021","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell. 43(6), 1964\u20131980 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Sekikawa, Y., Hara, K., Saito, H.: Eventnet: asynchronous recursive event processing. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3882\u20133891 (2019)","DOI":"10.1109\/CVPR.2019.00401"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: Hats: histograms of averaged time surfaces for robust event-based object classification. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1731\u20131740 (2018)","DOI":"10.1109\/CVPR.2018.00186"},{"key":"25_CR25","unstructured":"Tang, C., Wang, X., Huang, J., Jiang, B., Zhu, L., Zhang, J., Wang, Y., Tian, Y.: Revisiting color-event based tracking: A unified network, dataset, and metric (2022). arxiv:2211.11010, https:\/\/api.semanticscholar.org\/CorpusID:253734908"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Wang, L., Mostafavi, I.M., Ho, Y.S., Yoon, K.J.: Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10073\u201310082 (2019)","DOI":"10.1109\/CVPR.2019.01032"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, Y., Yuan, J., Lu, Y.: Space-time event clouds for gesture recognition: from RGB cameras to event cameras. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1826\u20131835 (2019)","DOI":"10.1109\/WACV.2019.00199"},{"issue":"3","key":"25_CR28","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1109\/TCYB.2023.3318601","volume":"54","author":"X Wang","year":"2024","unstructured":"Wang, X., Li, J., Zhu, L., Zhang, Z., Chen, Z., Li, X., Wang, Y., Tian, Y., Wu, F.: Visevent: reliable object tracking via collaboration of frame and event flows. IEEE Trans. Cybern. 54(3), 1997\u20132010 (2024)","journal-title":"IEEE Trans. Cybern."},{"key":"25_CR29","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1109\/LSP.2024.3381894","volume":"31","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Jiang, C., Jia, X., Guo, Y., Yu, L.: Event-based shutter unrolling and motion deblurring in dynamic scenes. IEEE Signal Process. Lett. 31, 1069\u20131073 (2024)","journal-title":"IEEE Signal Process. Lett."},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Weng, W., Zhang, Y., Xiong, Z.: Boosting event stream super-resolution with a recurrent neural network. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision\u2014ECCV 2022, pp. 470\u2013488, Cham (2022)","DOI":"10.1007\/978-3-031-20068-7_27"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, A., Yuan, L., Chaney, K., Daniilidis, K.: Ev-flownet: self-supervised optical flow estimation for event-based cameras. In: Proceedings of Robotics: science and Systems. Pittsburgh, Pennsylvania (2018)","DOI":"10.15607\/RSS.2018.XIV.062"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based learning of optical flow, depth, and egomotion. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 989\u2013997 (2019)","DOI":"10.1109\/CVPR.2019.00108"}],"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\/978-981-97-8685-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:21:46Z","timestamp":1730524906000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8685-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819786848","9789819786855"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8685-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 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"}}]}}