{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:51:24Z","timestamp":1779879084948,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981250","type":"print"},{"value":"9789819981267","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8126-7_37","type":"book-chapter","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T08:01:53Z","timestamp":1700812913000},"page":"470-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Event-Based Object Recognition Using Feature Fusion and\u00a0Spiking Neural Networks"],"prefix":"10.1007","author":[{"given":"Menghao","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panpan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runhao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Delbr\u00fcck, T., Linares-Barranco, B., Culurciello, E., Posch, C.: Activity-driven, event-based vision sensors. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 2426-2429. IEEE (2010)","DOI":"10.1109\/ISCAS.2010.5537149"},{"issue":"11","key":"37_CR2","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1038\/14819","volume":"2","author":"M Riesenhuber","year":"1999","unstructured":"Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019\u20131025 (1999)","journal-title":"Nat. Neurosci."},{"issue":"9","key":"37_CR3","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TNNLS.2014.2362542","volume":"26","author":"B Zhao","year":"2014","unstructured":"Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., Tang, H.: Feedforward categorization on AER motion events using cortex-like features in a spiking neural network. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 1963\u20131978 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"37_CR4","doi-asserted-by":"publisher","first-page":"2028","DOI":"10.1109\/TPAMI.2015.2392947","volume":"37","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Meyer, C., Etienne-Cummings, R., Posch, C., Thakor, N., Benosman, R.: Hfirst: a temporal approach to object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 2028\u20132040 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"37_CR5","doi-asserted-by":"publisher","first-page":"3649","DOI":"10.1109\/TNNLS.2019.2945630","volume":"31","author":"R Xiao","year":"2019","unstructured":"Xiao, R., Tang, H., Ma, Y., Yan, R., Orchard, G.: An event-driven categorization model for AER image sensors using multispike encoding and learning. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3649\u20133657 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Tang, T., Jiang, R., Yan, R., Tang, H.: An event-driven object recognition model using activated connected domain detection. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 3049-3056. IEEE (2020)","DOI":"10.1109\/SSCI47803.2020.9308321"},{"key":"37_CR7","first-page":"1308","volume":"34","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Ruan, H., Xing, D., Tang, H., Pan, G.: Effective AER object classification using segmented probability-maximization learning in spiking neural networks. Proc. AAAI Conf. Artif. Intell. 34, 1308\u20131315 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"7","key":"37_CR8","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1109\/TPAMI.2016.2574707","volume":"39","author":"X Lagorce","year":"2016","unstructured":"Lagorce, X., Orchard, G., Galluppi, F., Shi, B.E., Benosman, R.B.: Hots: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1346\u20131359 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR9","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1731-1740 (2018)","DOI":"10.1109\/CVPR.2018.00186"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Nan, Y., Xiao, R., Gao, S., Yan, R.: An event-based hierarchy model for object recognition. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2342-2347. IEEE (2019)","DOI":"10.1109\/SSCI44817.2019.9003142"},{"issue":"12","key":"37_CR11","doi-asserted-by":"publisher","first-page":"5300","DOI":"10.1109\/TNNLS.2020.2966058","volume":"31","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Pan, G., Ruan, H., Xing, D., Xu, Q., Tang, H.: Unsupervised AER object recognition based on multiscale spatio-temporal features and spiking neurons. IEEE Trans. Neural Netw. Learn. Syst. 31(12), 5300\u20135311 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Q., Xing, D., Tang, H., Ma, D., Pan, G.: Event-based action recognition using motion information and spiking neural networks. In: IJCAI, pp. 1743\u20131749 (2021)","DOI":"10.24963\/ijcai.2021\/240"},{"issue":"3","key":"37_CR13","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1038\/nn1643","volume":"9","author":"R G\u00fctig","year":"2006","unstructured":"G\u00fctig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420\u2013428 (2006)","journal-title":"Nat. Neurosci."},{"key":"37_CR14","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3389\/fnins.2015.00437","volume":"9","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015)","journal-title":"Front. Neurosci."},{"key":"37_CR15","doi-asserted-by":"publisher","first-page":"481","DOI":"10.3389\/fnins.2015.00481","volume":"9","author":"T Serrano-Gotarredona","year":"2015","unstructured":"Serrano-Gotarredona, T., Linares-Barranco, B.: Poker-DVS and mnist-DVS their history, how they were made, and other details. Front. Neurosci. 9, 481 (2015)","journal-title":"Front. Neurosci."},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Amir, A., et al.: A low power, fully event-based gesture recognition system. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7243\u20137252 (2017)","DOI":"10.1109\/CVPR.2017.781"},{"key":"37_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.590164","volume":"14","author":"Y Xing","year":"2020","unstructured":"Xing, Y., Di Caterina, G., Soraghan, J.: A new spiking convolutional recurrent neural network (scrnn) with applications to event-based hand gesture recognition. Front. Neurosci. 14, 590164 (2020)","journal-title":"Front. Neurosci."},{"key":"37_CR18","unstructured":"Shrestha, S.B., Orchard, G.: Slayer: spike layer error reassignment in time. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"He, W., et al.: Comparing SNNs and RNNs on neuromorphic vision datasets: similarities and differences. Neural Netw. 132, 108\u2013120 (2020)","DOI":"10.1016\/j.neunet.2020.08.001"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8126-7_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:37:39Z","timestamp":1709811459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8126-7_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"ISBN":["9789819981250","9789819981267"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8126-7_37","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"13 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}