基于深度卷积网络的监督粗到精光流测量算法

Meiyuan Fang, Yanghao Li, Yuxing Han, Jiangtao Wen
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引用次数: 1

摘要

光流测量是图像处理中的一个重要问题。光流估计有很多方法,包括传统的变分方法、基于深度学习的监督/无监督方法。在这项工作中,我们提出了一种基于深度卷积网络(CNN)的监督粗到精方法,该方法以端到端方式进行训练。在Flying Chairs、MPI、sinter Clean和Final、KITTI等标准光流基准数据集上对该方法进行了测试。实验结果表明,该框架能够在更小的网络结构下获得与先前方法相当的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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