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ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation [article]

Jinze Chen, Yang Wang, Yang Cao, Feng Wu, Zheng-Jun Zha
2022 arXiv   pre-print
To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner  ...  Specifically, based on the maximum sharpness criterion, ME module divides the input event into several segments by adaptive clustering in a motion compensating warp field, and captures the temporal correlation  ...  To this end, we propose a novel progressive optimization framework by optimizing the motion estimation and event denoising in a mutually reinforced manner.  ... 
arXiv:2203.11732v1 fatcat:fwqdk2tkpjcrjjqs4e4svd4msq

ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation

Jinze Chen, Yang Wang, Yang Cao, Feng Wu, Zheng-Jun Zha
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner  ...  Specifically, based on the maximum sharpness criterion, ME module divides the input event into several segments by adaptive clustering in a motion compensating warp field, and captures the temporal correlation  ...  To this end, we propose a novel progressive optimization framework by optimizing the motion estimation and event denoising in a mutually reinforced manner.  ... 
doi:10.1609/aaai.v36i1.19906 fatcat:4ynjmpr2f5fwlbbjgqkqo7pfme