Thanh Minh Nguyen

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In this paper, we present a new algorithm for pixel labeling and image segmentation based on the standard Gaussian mixture model (GMM). Unlike the standard GMM where pixels themselves are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account, the proposed method incorporates this spatial(More)
Finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian, has recently received great attention for image segmentation. A new finite Student's-t mixture model (SMM) is proposed in this paper. Existing models do not explicitly incorporate the spatial relationships between pixels. First, our model(More)
Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student's t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter, we propose a new algorithm to incorporate the merits of these two approaches. The(More)
Finite mixture models with symmetric distribution have been widely used for many computer vision and pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and nonsymmetric form. This paper presents a new nonsymmetric mixture model for image segmentation. The advantage of our method is that it is simple,(More)
This paper aims toward improving background suppression from video frames by incorporating multiresolution features in Gaussian mixture model (GMM). GMM has proven its place for background modeling due to its better applicability and robustness compared with other popular methods in literature. However, GMM fails in a number of situations such as noisy and(More)
The proposed work is targeted toward improving the Gaussian mixture model (GMM) for the background suppression-based moving object detection. The GMM has been widely used for moving object detection due to its high applicability. However, the GMM cannot properly model noisy or nonstationary backgrounds and fails to discriminate between the foreground and(More)