Rami Qahwaji

Recognition of off-line printed Arabic text using Hidden Markov Models

This paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages.

The Human Face of E-Business: Engendering Consumer Initial Trust Through the Use of Images of Sales Personnel on E-Commerce Web Sites

Business-to-consumer (B2C) e-commerce suffers from consumers’ lack of trust. This may be partly attributable to the lack of face-to-face interpersonal exchanges that provide trust behavior in conventional commerce. It was proposed that initial trust may be built by simulating face-to-face interaction. To test this, an extensive laboratory-based experiment was conducted to assess the initial trust in consumers using four online vendors’ Web sites with a variety of still and video images of sales personnel, both Western and Saudi Arabian. Initial trust was found to be enhanced for Web sites employing photographs and video clips compared to control Web sites lacking such images; also the effect of culture was stronger in the Saudi Arabian setting when using Saudi photos rather than Western photos.

A novel blind image watermarking technique for colour RGB images in the DCT domain using green channel

This paper presents a new algorithm for colour digital image watermarking. The 24 bits/pixel RGB images are used and the watermark is placed on the green channel of the RGB image. The green channel is chosen after an analytical investigation process was carried out using some popular measurement metrics. The analysis and embedding processes have been carried out using the discrete cosine transform DCT. The new watermarking method has shown to be resistant to JPEG compression, cropping, scaling, low-pass, median and removal attack. This algorithm produces more than 65 dB of average PSNR.

One Scan Connected Component Labeling Technique

This paper, presents a new component labeling algorithm which is based on scanning and labeling the objects in a single scan. The algorithm has the ability to test the four and eight connected branches of the object. This algorithm, which is fast and requires low memory allocation, can also process an image that contains large numbers of objects. The algorithm is used to scan the image from left to right and from top to bottom to find the unlabeled objects. A comparison analysis is performed with other component labeling algorithms. Our algorithm has shown an outstanding performance with respect to the processing time. A practical application with computer based mammography is also included.

Automatic detection and verification of solar features

A fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hit‐miss transform, watershed transform and Filling algorithm. An image‐enhancement technique is introduced to remove the limb‐darkening effect and intensity filtering is implemented followed by a modified region‐growing technique to detect the regions of interest (RoI). The algorithms are tested on H‐α and CA II K3‐line solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and non‐RoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199–210, 2005

Users’ Acceptance of Secure Biometrics Authentication System: Reliability and Validate of an Extended UTAUT Model

This paper presents current findings from cross-cultural studies investigating the adoption of new secure technology, based on fingerprint authentication systems to be applied to an e-commerce websites, within the perspective of Saudi culture. The aim of the study was to explore factors affecting users’ acceptance of biometric authentication systems. A large scale laboratory experiment of 306 Saudis was performed using a login fingerprint system to observe whether Saudis are practically and culturally enthusiastic to accept this technology. The findings were then examined to measure the reliability and validity along with applying a proposed conceptual framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with three moderating variables: age, gender and education level. The study model was adapted to meet the objectives of this study while adding other intrinsic factors such as self-efficiency and biometric system characteristics.

Automatic Sunspot Classification for Real-Time Forecasting of Solar Activities

Solar imaging is currently an active area of research. A fast hybrid system for the automated detection and classification of sunspot groups on MDI Continuum images using active regions data extracted from MDI Magnetogram images is presented in this paper. The system has three major stages: sunspots detection from MDI Continuum images, sunspots grouping and Mcintosh classification of sunspot groups. Image processing and machine learning are integrated in all these stages.

An efficient intelligent analysis system for confocal corneal endothelium images

A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patients cornea. A hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process images (including quality enhancement and noise reduction), detect cells, measure cell densities and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using a confocal microscope, and three abnormal confocal endothelium images associated with diseases have been investigated in the proposed system. Promising results are presented and the performance of this system is compared with manual and two morphological based approaches. The average differences between the manual and the automatic cell densities calculated using S-PSO and two other morphological based approaches is 5%, 7% and 13% respectively. The developed system will be deployable as a clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

Detection of Dust Storms Using MODIS Reflective and Emissive Bands

Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave better result than the Thermal Infrared Integrated Dust Index approach for dust storms detection over the Sahara. The trained ANN using data from the Sahara desert gave an accuracy of 0.88 when tested on data from the Gobi desert and managed to detect 90 out of the 96 dust storm events captured worldwide by Terra and Aqua satellites in 2011 that were classified as dusty images on NASA Earth Observatory.

Applied Signal and Image Processing: Multidisciplinary Advancements

Image and signal processing techniques are receiving increasing interest because of their numerous real-world applications. Data is now available in different forms, different wavelengths, and even in different dimensions, creating the need for novel multidisciplinary solutions for automated data processing and analysis.Applied Signal and Image Processing: Multidisciplinary Advancements highlights the growing multidisciplinary nature of signal and image processing by focusing on emerging applications and recent advances in well-established fields. This book covers state-or-the-art applications in both signal and image processing, which include optical communication and sensing, wireless communication management, face recognition and facial imaging, solar imaging and feature detection, fractal analysis, and video processing.