The outcome of consecutive training activities can overcome geopolitical instabilities, and yield a genuine change in approach of both regulators, medical administrators, medical staff and the public; as to the important contribution of palliative care services to the welfare of the patient and his/her family.

Text and not-text segmentation and text line extraction from document images are the most challenging problems of information indexing of Arabic document images such as books, technical articles, business letters and faxes in order to successfully process them in systems such as OCR. Researches on Arabic language related to documents digitization have been focusing on word and handwriting recognition. Few approaches have been proposed for layout analysis for Arabic scanned/captured documents. In this paper we present a page segmentation method that deals with the complexity of the Arabic language characteristics and fonts using the combination between two algorithms. The first method is the Run length Smoothing. The second method is the Connected Component Labeling algorithm for text and non-text classification using SVM. The combination of the two methods is based on Anding and Oring operations between the outputs of the two methods based on certain conditions. Then, dynamic horizontal projection based on dynamic updating of the threshold to commensurate with the noise associated with different documents and in between text lines. The performance evaluation is performed using manually generated ground truth representations from a dataset of Arabic document images captured using cameras and a hardware built for this purpose. Evaluation and experimental results demonstrate that the proposed text extraction method is independent from different document size, text size, font, shape, and is robust to Arabic document segmentation and text lines extraction.

Abstract-In this paper, the problem of multi-target tracking with single camera in complex scenes is addressed. A new approach is proposed for multi-target tracking problem that learns from hierarchy of convolution features. First fast Region-based Convolutional Neutral Networks is trained to detect pedestrian in each frame. Then cooperate it with correlation filter tracker which learns target's appearance from pretrained convolutional neural networks. Correlation filter learns from middle and last convolutional layers to enhances targets localization. However correlation filters fail in case of targets full occlusion. This lead to separated tracklets (mini-trajectories) problem. So a post processing step is added to link separated tracklets with minimum-cost network flow. A cost function is used, that depends on motion cues in associating short tracklets. Experimental results on MOT2015 benchmark show that the proposed approach produce comparable result against state-of-the-art approaches. It shows an increase 4.5 % in multiple object tracking accuracy. Also mostly tracked targets is 12.9% vs 7.5% against state-ofthe-art minimum-cost network flow tracker.

The objective of this research is to design and develop a flexible programmable video coprocessor. The processor targets applications for MPEG2 format. Five basic processing tasks have been identified as the main job of the coprocessor. They contribute to a wide variety of operations frequently needed by multimedia applications. These tasks are frame rate conversion (increase of frame rate or decrease of frame rate), resolution conversion, changing bits per pixel, filtering, and video compositing operations (rotations or mirroring of frame). The first phase of this project 1 presented a critical comprehensive study of the algorithms capable of performing these tasks in the DCT domain. In this paper the details of coprocessor design, implementation and the simulation of the chip are presented.

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