Estimating optical flow with cellular neural networks
Estimating optical flow with cellular neural networks
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DOI：
10.1002/(sici)1097-007x(199807/08)26:4
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发表时间：
1998-07
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影响因子：
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通讯作者：
Bertram E. Shi;T. Roska;L. Chua
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文献类型：
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作者：
Bertram E. Shi;T. Roska;L. Chua
细胞神经网络是一种局部互连的神经网络，在模拟VLSI中实现时能够进行高速计算。本文描述了一种CNN算法，用于从图像序列中估计光流。该算法基于图像运动分析的时空滤波方法，并被证明比以前提出的类似方法更准确地估计光流。该算法的两个创新特点是利用了超敏锐的生物模型，并开发了一种新的时空滤波器，比常用的时空Gabor滤波器更适合于图像运动分析。约翰威利父子有限公司
SUMMARY The cellular neural network is a locally interconnected neural network capable of high-speed computation when implemented in analog VLSI. This work describes a CNN algorithm for estimating the optical flow from an image sequence. The algorithm is based on the spatio-temporal filtering approach to image motion analysis and is shown to estimate the optical flow more accurately than a comparable approach proposed previously. Two innovative features of the algorithm are the exploitation of a biological model for hyperacuity and the development of a new class of spatio-temporal filter better suited for image motion analysis than the commonly used space—time Gabor filter. ( 1998 John Wiley & Sons, Ltd.