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)
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, 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)
Date Dedication To my parents and family, Dr. K. V. Reddy and family, and friends. For making it possible for me to complete my studies in graduate school. For the continued support and patience. For helping me to be a better person. For inspiring me to reach higher. For the unconditional love. For always being there for me. I dedicate this thesis to you.(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)