In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then,to fit the Gaussian mixtures to the histogram of image, the Expectation Maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.
In this paper, we propose a new embossing algorithm for gray images using Kalman filter. First, a 2D gray image is first converted to a one dimension vector; those vectors could be considered as a one-dimension discrete-time signal. Then, the performance of image filtering using Kalman filter for image is studied and according to its results, Canny edge detection operators are investigated to find edge map in a gray scale image. Finally, enhance contrast using histogram equalization has been applied. Compared with other conventional embossing method for images, it is an impressive experimental result using our proposed algorithm for gray image embossing. Practical results show that this algorithm can be exploited in different fields such as image pattern recognition.
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