Dimah Dera

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This paper presents a new level set method for image segmentation by integrating the level set formulation and the non-negative matrix factorization (NMF). The proposed model characterizes the histogram of the image by dividing the image into blocks and computing the histograms of the blocks as nonnegative combinations of basic histograms. This is achieved(More)
We propose a new non-parametric level set model for automatic image clustering and segmentation based on non-negative matrix factorization (NMF). We show that NMF: (i) clusters the image into distinct homogeneous regions and (ii) provides the local spatial distribution of each region within the image. Furthermore, NMF has a controllable resolution and can(More)
We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions,(More)
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