{"title":"基于深度卷积网络的监督粗到精光流测量算法","authors":"Meiyuan Fang, Yanghao Li, Yuxing Han, Jiangtao Wen","doi":"10.1109/MMSP.2018.8547130","DOIUrl":null,"url":null,"abstract":"The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement\",\"authors\":\"Meiyuan Fang, Yanghao Li, Yuxing Han, Jiangtao Wen\",\"doi\":\"10.1109/MMSP.2018.8547130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.\",\"PeriodicalId\":137522,\"journal\":{\"name\":\"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP.2018.8547130\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2018.8547130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement
The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.