Mustafa I. Jaber

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In this work, a region classification algorithm based on low-level features and probabilistic framework is proposed where skin, sky, and vegetation memory color classes are detected in digital images. A region's low-level features are extracted using a segmentation map of input image. Bayesian Network (BN) is used to classify memory color regions for smart(More)
In this paper, a Bayesian Network (BN) framework for unsupervised evaluation of image segmentation quality is proposed. This image understanding algorithm utilizes a set of given Segmentation Maps (SMs) ranging from under-segmented to over-segmented results for a target image, to identify the semantically meaningful ones and rank the SMs according to their(More)
We propose an image-understanding algorithm for identifying and ranking regions of perceptually relevant content in digital images. Global features that characterize relations between image regions are fused in a probabilistic framework to generate a region ranking map (RRM) of an arbitrary image. Features are introduced as maps for spatial position,(More)
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