Papers by Somaya Al-maadeed
Qatar Foundation Annual Research Conference, Nov 13, 2014
Qatar Foundation Annual Research Forum, Oct 19, 2012
Background and Objectives: There is high requirement of face and signature based multimodal biome... more Background and Objectives: There is high requirement of face and signature based multimodal biometric systems in various areas, such as banking, biometric systems and secured mobile phone operating systems. Few studies have been carried out in this area to enhance the performance of identification and authentication based on the fusion of those modalities. In multimodal biometric systems, the most common fusion approach is integration at the matching score level, but it is necessary to compare this strategy of ...
A system for Arabic writer identification using grapheme features and k-nearest neighbor classifi... more A system for Arabic writer identification using grapheme features and k-nearest neighbor classifier is built using Matlab programming language. The results of our preliminary study reveal that unknown writers can be identified by using edge base directional features and text-dependent method; however the simple system approach needs improvement to satisfy the requirements of real data. This project works on the following improvements: First a database of text- independent Arabic handwritten pages from around 100 different writers is gathered and used as a test bed. Then, features will be extracted from writers' handwriting. Prior to feature extraction, preprocessing operations is applied to documents to remove the background. In this research, we build an interactive background removal interface. Then the multi-scale edge-hinge features and grapheme features will be extracted from the handwritten pages. The classification will be performed by a k-nearest neighbor classifier. The project studies the performance of the new features, and recognition operations on Arabic text, on the identification rate of writers. Matlab programming language is used to write the programs for this project. This project aims at building an Arabic writer identification system consisting of three main processes: an interactive preprocessing to remove documents background, a feature extraction process to extract feature vector, and a classification process. The three processes will be implemented on the training and testing phase of the system.
Hidden Markov models (HMM) have been used with some success in recognizing printed Arabic words. ... more Hidden Markov models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a model discriminant HMM is presented. A complete system able to classify Arabic handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove some of variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the lines in the skeleton is extracted. Then a classification process based on the HMM approach is used. The output is a word in the dictionary. A detailed experiment is carried out and successful recognition results are reported.
This paper evaluates the performance of edge-based directional probability distributions as featu... more This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of non-angular features. It is noted that the joint probability distribution of the angle combination of two "hinged" edge fragments outperforms all other individual features. Combining features may improve the performance. Limitations of the method pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (upper-vs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.
The main steps of document processing have been reviewed, especially those implemented on Arabic ... more The main steps of document processing have been reviewed, especially those implemented on Arabic writing. The techniques used in this research, such as Vector Quantization (VQ), Hidden Markov Models (HMM), and Induction of Decision Trees (ID3) have been considered, as well as reviewing pre-processing and feature extraction used in Arabic writing.
Knowledge Based Systems, 2004
A complete scheme for unconstrained Arabic handwritten word recognition based on a multiple hidde... more A complete scheme for unconstrained Arabic handwritten word recognition based on a multiple hidden Markov models (HMM) is presented. The overall engine of this combination of a global feature scheme with a HMM module, is a system able to classify Arabic-handwritten words. The system first removes some of the variation in the images. Next, it codes the skeleton and edge of the word such that features are extracted. Then, a rule-based classifier is used as a global recognition engine. Finally, for each group, the HMM ...
Abstract: This research aims to use geometrical features and neural networks to automatically rec... more Abstract: This research aims to use geometrical features and neural networks to automatically recognize (read) off-line handwritten Arabic words. The nature of handwritten Arabic characters and hence the problems that could be faced when automatically (optically) recognizing them are discussed. This research concentrates on the feature extraction process, ie extraction of the main geometrical features of each of the extracted handwritten Arabic characters.
Abstract We propose a new and efficient method to develop secure image-encryption techniques. The... more Abstract We propose a new and efficient method to develop secure image-encryption techniques. The new algorithm combines two techniques: encryption and compression. In this technique, a wavelet transform was used to decompose the image and decorrelate its pixels into approximation and detail components. The more important component (the approximation component) is encrypted using a chaos-based encryption algorithm.
Machine Vision and …
This paper proposes an automatic classification system for the use in prostate cancer diagnosis. ... more This paper proposes an automatic classification system for the use in prostate cancer diagnosis. The system aims to detect and classify prostatic tissue textures captured from microscopic samples taken from needle biopsies. Biopsies are usually analyzed by a trained pathologist with different grades of malignancy typically corresponding to different structural patterns as well as apparent textures. In the context of prostate cancer diagnosis, four major groups have to be accurately recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been proposed as a new image acquisition modality which unlike conventional RGB-based light microscopy allows the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. Many features in the initial feature set are irrelevant to the classification task and are correlated with each other, resulting in an increase in the computational complexity and a reduction in the recognition rate. In this paper, a Round-Robin (RR) sequential forward selection RR-SFS is used to address these problems. RR is a technique for handling multi-class problems with binary classifiers by training one classifier for each pair of classes. The experimental results demonstrate this finding when compared with classical method based on the multiclass SFS and other ensemble methods such as bagging/boosting with decision tree (C4.5) classifier where it is shown that RR-SFS method achieves the best results with a classification accuracy of 99.9%.
Uploads
Papers by Somaya Al-maadeed