A rectilinear Gaussian model for estimating straight-line parameters |
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| Affiliation: | 1. Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei 10617, Taiwan;2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan;1. School of Computer & Information, Hefei University of Technology, Hefei 230009, China;2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;3. Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China;1. State Key laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, PR China;2. Department of Mathematics, Zhejiang University, Hangzhou 310027, PR China |
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| Abstract: | For characterizing straight lines in defocused images, a rectilinear Gaussian model (RGM) is proposed. Based on this model, a novel method for estimating the parameters of straight lines is presented. This method, called gray-scale least square (GLS) method, directly deals with gray-scale image data without requiring any preprocessing and hence no additional noise is introduced. Furthermore, the method is able to simultaneously estimate four parameters of straight lines by performing the algorithm only once, while two parameters can be typically estimated by traditional method. Besides this, all parameters are given in closed-form solution. In order to illustrate the effectiveness of RGM and the GLS method, the experiments are performed on a set of artificial images and natural images. The experimental results show that the GLS method outperforms the traditional method from the point of view of sensitivity to noise and accuracy of parameter estimation. |
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