DEPTH FROM DEFOCUS USING RADIAL BASIS FUNCTION NETWORKS

摘要：

In range finding, the depth from defocus (DFD) is a simple and effective method.We use the DFD method to analyze the defocused images to obtain depth information using Gaussian blurred function.In order to find the range of objects, a sigma value of the Gaussian function due to edges out of focus is necessary.Since the sigma value of the Gaussian function depicts on the intensity of images grabbed by imaging devices, we employ an approximate method, the radial basis function networks (RBFN), to approach the sigma value directly in the spatial domain.The RBFN regularizes the center position and the sigma value of the Gaussian function to fit the profile of the defocused image by three layers of neural networks based on the radial basis function.It has accurate ranging results with less than 8％ of the root mean square error in sigma value approaching and 5％ of the relative error in ranging, imaging system ranges from 220mm to 355mm and focuses at 400mm.

关键词： Depth from defocus Radial basis function networks Neural networks

作者: SHYH-MING JONG

作者单位: Department of Mechanical Engineering, Lee-Ming Institute of Technology, Taipei County, Taiwan

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1888-1893

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）