EEE5046 Modern Signal Processing (2019Fall)
Instructor : Prof. Xiaoying Tang
Team ID : 1
Team Member: Zhenwei Yao, Xinrao Li, Yaoyao Ji, Man Liu, Wei Gu, Haoyu Miao
@Southern University of Science and Technology (SUSTech) , Shenzhen

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The goal of segmentation is to partition an image into regions each of which has a reasonably homogeneous visual appearance or which corresponds to objects or parts of objects (ForsythandPonce,2003). Eachpixelinanimageisapointina3-dimensionalspace comprising the intensities of the red, blue, and green channels, and our segmentation algorithm simply treats each pixel in the image as a separate data point. We see that for a given value of K, the algorithm is representing the image using a palette of only K colours

The segmentation algorithm used in this project is based on a parametric model in which the probability density function (PDF) is a mixture of Gaussian density functions. is the total number of the Gaussian components. As a model-based segmentation algorithm, its performance is influenced by the shape of the image histogram and the accuracy of the estimates of the model parameters[1].

Fig.1 Input image and the Histogram

[1] Gupta, Lalit, and Thotsapon Sortrakul. "A Gaussian-mixture-based image segmentation algorithm." Pattern Recognition 31, no. 3 (1998): 315-325.

feature similarity index (FSIM) structure similarity index (SSIM) peak signal noise ratio (PSNR)

Fig.10 Gantt Chart