People with special-needs face a variety of different challenges and barriers that isolate them from their surroundings. Nowadays, several assistive technologies have been developed to reduce many of these barriers and simplify the communication between special-needs persons and the surrounding environment. However, few frameworks are presented to support them in the Arabic region either due to the lack of resources or the complexity of the Arabic language. The main goal of this work is to present a mobile-based framework that will help Arabic deaf people to communicate 'on the go' easily with virtually any one without the need of any specific devices or support from other people. The framework utilizes the power of cloud computing for the complex processing of the Arabic text. The speech processing produced a cartoon avatar showing the corresponding Egyptian Arabic Sign Language on the mobile handset of the deaf person. Ó 2016 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).
Abstract-Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. Automatic registration of remote-sensing images is a difficult task as it must deal with the intensity changes and variation of scale, rotation and illumination of the images. This paper proposes image registration technique of multi-view, multi-temporal and multispectral remote sensing images. Firstly, a preprocessing step is performed by applying median filtering to enhance the images. Secondly, the Steerable Pyramid Transform is adopted to produce multi-resolution levels of reference and sensed images; then, the Scale Invariant Feature Transform (SIFT) is utilized for extracting feature points that can deal with the large variations of scale, rotation and illumination between images .Thirdly, matching the features points by using the Euclidian distance ratio; then removing the false matching pairs using the RANdom SAmple Consensus (RANSAC) algorithm. Finally, the mapping function is obtained by the affine transformation. Quantitative comparisons of our technique with the related techniques show a significant improvement in the presence of large scale, rotation changes, and the intensity changes. The effectiveness of the proposed technique is demonstrated by the experimental results.
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