Mustafa I. Jaber

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This paper presents an efficient numerical formulation for the prediction of realistic postures. This problem is defined by the method (or procedure) used to predict the posture of a human, given a point in the reachable space. The exposition addresses (1) the determination whether a point is reachable (i.e., does is it exist within the reach envelope) and(More)
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)
With the growth of the multimedia and cellular technology over the past decades, the demand for digital information increases dramatically. This enormous demand poses difficulties for the current technology to handle speech compression. One approach to overcome this problem is to compress the information by removing the redundancies present in it. This is a(More)
In this paper, we present an image understanding algorithm for automatically identifying and ranking different image regions into several levels of importance. Given a color image, specialized maps for classifying image content namely: weighted similarity, weighted homogeneity, image contrast and memory colors are generated and combined to provide a metric(More)
DNA microarrays are a powerful experimental tool for the detection of specific genomic sequences and are invaluable to a broad array of applications: clinical diagnosis, personalized medicine, drug research and development, gene therapy, food control technologies, and environmental sciences. Alloimmunization to human platelet antigens (HPAs) is commonly(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)