Face detection and recognition has been introduced in many real world applications. Several algorithms have been implemented for either detection or recognition. In this paper, a novel algorithm, which simultaneously detects and recognizes facial images employing the same method, is presented. The proposed algorithm is based on a new 2D representation for the Histogram of Oriented Gradients (2D

Person Name extraction from Arabic text is a challenging task. While most existing Arabic texts are written in Modern Standard Arabic Text (MSA) the volume of Arabic Colloquial text is increasing progressively with the wide spread use of social media examples of which are Facebook, Google Moderator and Twitter. Previous work addressed extracting persons' names from MSA text only and especially

The wide spread use of social media tools and forums has led to the production of textual data at unprecedented rates. Without summarization, classification or other form of analysis, the sheer volume of this data will often render it useless and human analysis on this scale is next to impossible. The work presented in this paper focuses on investigating an approach for classifying large volumes

Tagged Magnetic Resonance (MR) images are considered the gold standard for evaluating the cardiac regional function. Nevertheless, the low myocardium-to-blood contrast in tagged MR images prevents accurate segmentation of the myocardium, and hence, hinders the quantitative assessment of the global function of the heart. In this work, a method for enhancing the myocardium-to-blood contrast in

Advances in Brain-computer Interface (BCI) technology have opened the door to assisting millions of people worldwide with disabilities. In this work, we focus on assessing brain attention state that could be used to selectively run an application on a hand-held device. We examine different classification techniques to assess brain attention state. Spectral analysis of the recorded EEG activity was

This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time with enhanced accuracy and is suitable for UAV imagery. The framework is deployed for on-board processing and tested over datasets collected by our UAV system. The framework is based on image feature processing and projective geometry and is carried out on the following stages

The use of Unmanned Aerial Vehicles (UAVs) for reconnaissance and surveillance applications has been steadily growing over the past few years. The operations of such largely autonomous systems rely primarily on the automatic detection and tracking of targets of interest. This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time and is

In this work, we present a new method for analyzing cardiac tagged Magnetic Resonance Imaging (tMRI). The method combines two major tracking techniques: Harmonic Phase (HARP) and Optical Flow (OF). The results of the two techniques are fused together to accurately estimate the displacement of each myocardium point. The developed methods were tested using numerical MRI phantom at different SNR

The contribution of this paper is twofold. First, we provide new theoretical insights into the relationship between a string and its reverse: If the Burrows-Wheeler transform (BWT) of a string has been computed by sorting its suffixes, then the BWT and the longest common prefix array of the reverse string can be derived from it without suffix sorting. Furthermore, we show that the longest common

The strain encoding (SENC) technique encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the