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Simultaneous Optical Flow and Intensity Estimation from an Event Camera. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_5_1","first-page":"4122","article-title":"Less Data Same Information for Event-Based Sensors","volume":"18","author":"Barrios-Avil\u00e9s J","year":"2018","unstructured":"J Barrios-Avil\u00e9s , A Rosado-Mu\u00f1oz , L. Medus , M Bataller-Mompe\u00e1n , and J Guerrero-Mart\u00ednez . 2018 . Less Data Same Information for Event-Based Sensors : A Bioinspired Filtering and Data Reduction Algorithm. Sensors 18 , 12 (2018), 4122 --. J Barrios-Avil\u00e9s, A Rosado-Mu\u00f1oz, L. Medus, M Bataller-Mompe\u00e1n, and J Guerrero-Mart\u00ednez. 2018. Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm. Sensors 18, 12 (2018), 4122--.","journal-title":"A Bioinspired Filtering and Data Reduction Algorithm. 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Handbook of Systemic Autoimmune Diseases 1 4 (2009)."},{"key":"e_1_3_2_1_24_1","volume-title":"HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition","author":"Lagorce X.","year":"2017","unstructured":"X. Lagorce , G. Orchard , F. Gallupi , B. E. Shi , and R. Benosman . 2017 . HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition . IEEE Transactions on Pattern Analysis Machine Intelligence ( 2017), 1--1. X. Lagorce, G. Orchard, F. Gallupi, B. E. Shi, and R. Benosman. 2017. HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition. IEEE Transactions on Pattern Analysis Machine Intelligence (2017), 1--1."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2308551"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2007.914337"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2015.7168735"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings \/ CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Maqueda A. I.","year":"2018","unstructured":"A. I. Maqueda , A. Loquercio , G. Gallego , N. Garcia , and D. Scaramuzza . 2018. Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars . Proceedings \/ CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( 2018 ). A. I. Maqueda, A. Loquercio, G. Gallego, N. Garcia, and D. Scaramuzza. 2018. Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars. Proceedings \/ CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018)."},{"key":"e_1_3_2_1_29_1","volume-title":"Event- Based Moving Object Detection and Tracking. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS).","author":"Mitrokhin A.","unstructured":"A. Mitrokhin , C Ferm\u00fcller , C. Parameshwara , and Y. Aloimonos . 2019 . Event- Based Moving Object Detection and Tracking. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). A. Mitrokhin, C Ferm\u00fcller, C. Parameshwara, and Y. Aloimonos. 2019. Event- Based Moving Object Detection and Tracking. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2346763"},{"key":"e_1_3_2_1_31_1","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Qi C. R.","year":"2017","unstructured":"C. R. Qi , H. Su , K. Mo , and L. J. Guibas . 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation . 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ( 2017 ). C. R. Qi, H. Su, K. Mo, and L. J. Guibas. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"N. J. Sanket C. M. Parameshwara C. D. Singh A. V. Kuruttukulam C Ferm\u00fcller D. Scaramuzza and Y Aloimonos. 2019. EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras. (2019).  N. J. Sanket C. M. Parameshwara C. D. Singh A. V. Kuruttukulam C Ferm\u00fcller D. Scaramuzza and Y Aloimonos. 2019. EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras. (2019).","DOI":"10.1109\/ICRA40945.2020.9196877"},{"key":"e_1_3_2_1_33_1","volume-title":"HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","author":"Sironi A.","unstructured":"A. Sironi , M. Brambilla , N. Bourdis , X. Lagorce , and R. Benosman . 2018 . HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. A. Sironi, M. Brambilla, N. Bourdis, X. Lagorce, and R. Benosman. 2018. HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_34_1","first-page":"137","article-title":"On event-based optical flow detection","volume":"9","author":"Tobias B.","year":"2015","unstructured":"B. Tobias , T. Stephan , and N. Heiko . 2015 . On event-based optical flow detection . Frontiers in Neuroscience 9 (2015), 137 --. B. Tobias, T. Stephan, and N. Heiko. 2015. On event-based optical flow detection. Frontiers in Neuroscience 9 (2015), 137--.","journal-title":"Frontiers in Neuroscience"},{"key":"e_1_3_2_1_35_1","volume-title":"EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Wang Y.","unstructured":"Y. Wang , B. Du , Y. Shen , K. Wu , G. Zhao , J. Sun , and H. Wen . 2020 . EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Y. Wang, B. Du, Y. Shen, K. Wu, G. Zhao, J. Sun, and H. Wen. 2020. EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_36_1","volume-title":"Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging. In CVPR","author":"Wang Z. W.","year":"2020","unstructured":"Z. W. Wang , P. Duan , O. Cossairt , A. Katsaggelos , and B. Shi . 2020 . Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging. In CVPR 2020 . Z. W.Wang, P. Duan, O. Cossairt, A. Katsaggelos, and B. Shi. 2020. Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging. 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