Abdelmalik Taleb-Ahmed

QRS complex detection based on multi wavelet packet decomposition

Abstract We present in this paper a wavelet packet based QRS complex detection algorithm. Our proposed algorithm consists of a particular combination of two vectors obtained by applying a designed routine of QRS detection process using ‘haar’ and ‘db10’ wavelet functions respectively. The QRS complex detection routine is based on the histogram approach where our key idea was to search for the node with highest number of histogram coefficients, at center, which we assume that they are related to the iso-electric baseline whereas the remaining least number coefficients reflect the R waves peaks. Following a classical approach based of a calculated fixed threshold, the possible QRS complexes will be determined. The QRS detection complex algorithm has been applied to the whole MIT-BIH arrhythmia Database to assess its robustness. The algorithm reported a global sensitivity of 98.68%, positive predictive value of 97.24% and a percentage error of 04.12%. Eventhough, the obtained global results are not as excellent as expected, we have demonstrate that our designed QRS detection algorithm performs good on a partial selected high percentage of the whole database, e.g., the partial results, obtained when applying the algorithm on 85.01% of the whole MIT-BIH arrhythmia Database, are 99.14% of sensitivity, 98.94% of positive predictive value and 01.92% of percentage error.

Social spiders optimization and flower pollination algorithm for multilevel image thresholding

We present an empirical comparison of two new meta-heuristics SSO and FP.Real test images were used to perform thresholding using Otsus method and Kapurs entropy.Compared algorithms were SSO, FP, PSO, BAT.Comparisons were made according to the fitness values, PSNR and SSIM.SSO shows superior performance in convergence and in quality terms. In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapurs method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithmprovides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem.

An efficient speech recognition system in adverse conditions using the nonparametric regression

General Regression Neural Networks (GRNN) have been applied to phoneme identification and isolated word recognition in clean speech. In this paper, the authors extended this approach to Arabic spoken word recognition in adverse conditions. In fact, noise robustness is one of the most challenging problems in Automatic Speech Recognition (ASR) and most of the existing recognition methods, which have shown to be highly efficient under noise-free conditions, fail drastically in noisy environments. The proposed system was tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multispeakers babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 database. The proposed scheme was successfully compared to the similar recognizers based on the Multilayer Perceptrons (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results showed that the use of nonparametric regression with an appropriate smoothing factor (spread) improved the generalization power of the neural network and the global performance of the speech recognizer in noisy environments.

Efficient Generation of

This paper presents a new procedure for generating 1/fbeta noise sequences by filtering Gaussian white noise through a recursive filter approximating a discrete-time fractional order integrator. Power series expansion and deterministic signal modeling techniques were combined to determine a rational transfer function to approximate the ideal discrete fractional transfer function resulting from Al-Alaouis rule raised to the power of the fractional order of integration. The proposed approach is computationally more efficient and more accurate than infinite impulse response truncation. Numerical results are provided to demonstrate the performance of the proposed method.

1/f^{\beta}

Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in many real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Phase Quantization (ML-LPQ) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.

Noise Using Signal Modeling Techniques

The paper fits the topics of the special issue as it deals with gender classification.A review of 13 recent and popular local binary patterns (LBP) variants is presented.A comparative analysis on two problems (gender and texture classification) is given.Extensive experiments showed that basic LBP generalizes well to different problems.The best results are obtained by BSIF but at the cost of higher computational time. Among very popular local image descriptors which has shown interesting results in extracting soft facial biometric traits is the local binary patterns (LBP). LBP is a gray-scale invariant texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. LBP labels can be regarded as local primitives such as curved edges, spots, flat areas etc. These labels or their statistics, most commonly the histogram, can then be used for further image analysis. Due to its discriminative power and computational simplicity, the LBP methodology has already attained an established position in computer vision. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors. Since its introduction, LBP has inspired plenty of new methods, thus revealing that texture based region descriptors can be very efficient in representing different images. Nowadays, many LBP variants can be found in the literature. This article reviews 13 variants and provides a comparative analysis on two different problems (gender and texture classification) using benchmark databases. The experiments show that basic LBP provides good results and generalizes well to different problems and hence can be a good starting point when trying to find an optimal variant for a given application. The best results are obtained with BSIF (binarized statistical image features) but at the cost of higher computational time compared to basic LBP. Furthermore, experiments on combining three best performing descriptors are conducted, pointing out useful insight into their complementarity.

Facial age estimation and gender classification using multi level local phase quantization

This work describes a general method of acceleration of the convergence of the Hough transform based, on the one hand, on an improvement of the image analysis speed, and, on the other hand, on the space undersampling of the image. This method is used in image processing to extract lines, circles, ellipses or arbitrary shapes. The results presented are applied to the detection of straight-line segments and ellipses, but can be extended to any type of transform.

Gender and texture classification

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database.

Applying the Hough transform pseudo-linearity property to improve computing speed

In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water birds sound, a tonal region detector TRD using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction MFCC-SS were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.

Facial age estimation using BSIF and LBP

Abstract In the diagnosis using MRI images, image segmentation techniques play a key role. Existing segmentation methods are generally based on basic image features such as grey level and texture. However, these methods cannot effectively identify physical significance of segmented objects from image because these basic image features such as grey level cannot take into consideration specialized medical knowledge, which is important when doctors study them manually using their own vision and experience. To deal with this problem, many tissue classification systems have been developed by integrating specific medical knowledge. All of these systems focus on specific applications and cannot be normalized and structured. Therefore, adaption of such systems to other contexts is rather difficult. In this paper, we propose an intelligent generalized tissue classification system which combines both the Fuzzy C-Means algorithm and the qualitative medical knowledge on geometric properties of different tissues. In this system, a general geometric model is proposed for formalizing non-structured and non-normalized medical knowledge from various medical images. This system has been successfully applied to the classification of human thigh, crus, arm, forearm, and brain in MRI images.