Somaya Al-Maadeed

The ICDAR2011 Arabic Writer Identification Contest

Arabic writer identification is a very active research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in Arabic writer identification. This competition was hosted by Kaggle, it has attracted thirty participants from both academia and industry. This paper gives details on this competition, including the evaluation procedure, description of participating methods and their performances.

A new chaos-based image-encryption and compression algorithm

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. This algorithm produces a cipher of the test image that has good diffusion and confusion properties. The remaining components (the detail components) are compressed using a wavelet transform. This proposed algorithm was verified to provide a high security level. A complete specification for the new algorithm is provided. Several test images are used to demonstrate the validity of the proposed algorithm. The results of several experiments show that the proposed algorithm for image cryptosystems provides an efficient and secure approach to real-time image encryption and transmission.

FPGA Implementation of Orthogonal Matching Pursuit for Compressive Sensing Reconstruction

In this paper, we present a novel architecture based on field-programmable gate arrays (FPGAs) for the reconstruction of compressively sensed signal using the orthogonal matching pursuit (OMP) algorithm. We have analyzed the computational complexities and data dependence between different stages of OMP algorithm to design its architecture that provides higher throughput with less area consumption. Since the solution of least square problem involves a large part of the overall computation time, we have suggested a parallel low-complexity architecture for the solution of the linear system. We have further modeled the proposed design using Simulink and carried out the implementation on FPGA using Xilinx system generator tool. We have presented here a methodology to optimize both area and execution time in Simulink environment. The execution time of the proposed design is reduced by maximizing parallelism by appropriate level of unfolding, while the FPGA resources are reduced by sharing the hardware for matrix-vector multiplication across the data-dependent sections of the algorithm. The hardware implementation on the Virtex6 FPGA provides significantly superior performance in terms of resource utilization measured in the number of occupied slices, and maximum usable frequency compared with the existing implementations. Compared with the existing similar design, the proposed structure involves 328 more DSP48s, but it involves 25802 less slices and 1.85 times less computation time for signal reconstruction with N = 1024, K = 256, and m = 36, where N is the number of samples, K is the size of the measurement vector, and m is the sparsity. It also provides a higher peak signal-to-noise ratio value of 38.9 dB with a reconstruction time of 0.34 μs, which is twice faster than the existing design. In addition, we have presented a performance metric to implement the OMP algorithm in resource constrained FPGA for the better quality of signal reconstruction.

Recognition of Off-Line Handwritten Arabic Words Using Neural Network

Neural network (NN) has been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a neural network is proposed and discussed. The overall engine of this combination of a global feature scheme with a NN is a system able to classify Arabic-handwritten words of one hundred different writers. The system first attempts to remove some of the 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 strokes in the skeleton is extracted. Then, a classification process based on the artificial NN classifier is used as global recognition engine, to classify the Arabic words. The output is a word in the dictionary. A detailed experiment is carried out, and successful recognition results are reported

Off-line writer identification using an ensemble of grapheme codebook features

A novel approach is proposed for off-line writer identification.The proposed approach utilizes an ensemble of codebook grapheme features.Kernel discriminant analysis is employed for dimensionality reduction.Experiments are conducted using publicly available writer identification data sets.The proposed technique provides a very accurate and efficient solution. Off-line writer identification is the process of matching a handwritten sample with its author. Manual identification is very time-consuming because it requires a meticulous comparison of character shape details. Consequently the automation of writer identification has become an important area of research interest. The codebook (or bag of features) approach is a state-of-the-art computerized technique for writer identification. One way to achieve a high identification rate is to expose the personalized set of character shapes, or allographs, that a writer has adopted over the years. The main problem associated with this approach is the extremely large of number of points of interest that are generated. In this paper we extend the basic model to include an ensemble of codebooks. Additionally, Kernel discriminant analysis using spectral regression (SR-KDA) is used as a dimensionality reduction technique in order to avoid over-fitting. Fusion of multiple codebooks is shown to increase the identification rate by 11% compared with a single codebook approach.

A set of geometrical features for writer identification

Writer identification is an important field in the forensic document examination. We propose in this paper a set of geometrical features that makes it possible to characterize writers. They include directions, curvatures and tortuosities. We show how these features can be combined with edge based directional features as well as chain code based features. Evaluation of the method is performed on the IAM handwriting database.

Memory Footprint Reduction for Power-Efficient Realization of 2-D Finite Impulse Response Filters

We have analyzed memory footprint and combinational complexity to arrive at a systematic design strategy to derive area-delay-power-efficient architectures for two-dimensional (2-D) finite impulse response (FIR) filter. We have presented novel block-based structures for separable and non-separable filters with less memory footprint by memory sharing and memory-reuse along with appropriate scheduling of computations and design of storage architecture. The proposed structures involve L times less storage per output (SPO), and nearly L times less energy consumption per output (EPO) compared with the existing structures, where L is the input block-size. They involve L times more arithmetic resources than the best of the corresponding existing structures, and produce L times more throughput with less memory band-width (MBW) than others. We have also proposed separate generic structures for separable and non-separable filter-banks, and a unified structure of filter-bank constituting symmetric and general filters. The proposed unified structure for 6 parallel filters involves nearly 3.6L times more multipliers, 3L times more adders, (N2-N+2) less registers than similar existing unified structure, and computes 6L times more filter outputs per cycle with 6L times less MBW than the existing design, where N is FIR filter size in each dimension. ASIC synthesis result shows that for filter size (4 × 4), input-block size L=4, and image-size (512 × 512), proposed block-based non-separable and generic non-separable structures, respectively, involve 5.95 times and 11.25 times less area-delay-product (ADP), and 5.81 times and 15.63 times less EPO than the corresponding existing structures. The proposed unified structure involves 4.64 times less ADP and 9.78 times less EPO than the corresponding existing structure.

Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery

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%.

Combining Fisher locality preserving projections and passband DCT for efficient palmprint recognition

In this paper a new graph based approach referred to as Fisher Locality Preserving Projections (FLPP) is proposed for efficient palmprint recognition. The technique employs two graphs with the first being used to characterize the within-class compactness and the second being dedicated to the augmentation of the between-class separability. In addition, a Passband Discrete Cosine Transform (PBDCT) is used for dimensionality reduction and feature extraction. This process makes the palmprint features more robust against inherent degradations of palmprint images. By applying an FLPP, only the most discriminant and stable palmprint features are retained. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area one should carefully consider this fact when performing the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows the efficient extraction of the whole palm area ignoring all the undesirable parts, such as the fingers and background. The experimental results demonstrate the effectiveness of the proposed method even for highly degraded palmprint images. An Equal Error Rate (EER) of 0.48% has been obtained on a database of 4000 palmprint images. HighlightsPropose a ROI method to extract complete palm?s area to capture all palm information.Investigate palmprint feature extraction using Passband DCT (PDCT).Show that PDCT is an efficient dimensionality reduction in the presence of degradations.Propose a new linear subspace technique to maximize between-class scatter.Propose a new linear subspace method to compact effectively the within-class scatter.

On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals

This paper proposes new time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. These features are obtained by translating and combining the most relevant time-domain and frequency-domain features into a joint time-frequency domain in order to improve the performance of EEG seizure detection and classification of non-stationary EEG signals. The optimal relevant translated features are selected according maximum relevance and minimum redundancy criteria. The experiment results obtained on real EEG data, show that the use of the translated and the selected relevant time-frequency features improves significantly the EEG classification results compared against the use of both original time-domain and frequency-domain features.