![]() ![]() ![]() Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking.Īccurate medical images analysis plays a vital role for several clinical applications. We demonstrate the effectiveness of the developed segmentation method through several experiments. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. For model's parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm's performance. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. ![]()
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