Randa Atta

Bio: Randa Atta is an academic researcher from Port Said University. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 9, co-authored 29 publications receiving 245 citations. Previous affiliations of Randa Atta include University of Essex & Suez Canal University.

A high payload steganography mechanism based on wavelet packet transformation and neutrosophic set

Abstract: In this paper a steganographic method is proposed to improve the capacity of the hidden secret data and to provide an imperceptible stego-image quality. The proposed steganography algorithm is based on the wavelet packet decomposition (WPD) and neutrosophic set. First, an original image is decomposed into wavelet packet coefficients. Second, the generalized parent–child relationships of spatial orientation trees for wavelet packet decomposition are established among the wavelet packet subbands. An edge detector based on the neutrosophic set named (NSED) is then introduced and applied on a number of subbands. This leads to classify each wavelet packet tree into edge/non-edge tree to embed more secret bits into the coefficients in the edge tree than those in the non-edge tree. The embedding is done based on the least significant bit substitution scheme. Experimental results demonstrate that the proposed method achieves higher embedding capacity with better imperceptibility compared to the published steganographic methods.

Brightness preserving based on singular value decomposition for image contrast enhancement

Abstract: This paper proposes a modification of the low contrast enhancement techniques that are based on the singular value decomposition (SVD) for preserving the mean brightness of a given image. Although the SVD-based techniques enhance the low contrast images by scaling its singular value matrix, they may fail to produce satisfactory results for some low contrast images. With the proposed method, the weighted sum of singular matrices of the input image and its global histogram equalization (GHE) image is calculated to obtain the singular value matrix of the equalized image. Simulation results show that the proposed method preserves the image brightness more precisely and enhances it with relatively negligible visual artifacts. It outperforms the conventional image equalization such as GHE and local histogram equalization (LHE), as well as the SVD techniques that based on scaling its singular value both qualitatively and quantitatively.

Low-contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition

Abstract: This study presents a satellite image contrast enhancement technique based on the discrete cosine transform (DCT) pyramid and singular value decomposition (SVD), in contrast to the methods based on wavelet decomposition and SVD which could fail to produce satisfactory results for some low-contrast images. With the proposed method, an input image is decomposed into a low sub-band image and reversed L-shape blocks containing the high-frequency coefficients of the DCT pyramid. The singular value matrix of the equalised low sub-band image is then estimated from the combination between the singular matrix of the low sub-band image and the singular matrix of its global histogram equalisation. The qualitative and quantitative performances of the proposed technique are compared with those of conventional image equalisation such as general histogram equalisation and local histogram equalisation, as well as some state-of-the-art techniques such as singular value equalisation technique. Moreover, the proposed technique is contrasted against the technique based on the discrete wavelet transform (DWT) and SVD (DWT-SVD) as well as the technique based on DCT-SVD. The experimental results show that the proposed method outperforms both conventional and the state-of-the-art techniques.

Low-memory requirement and efficient face recognition system based on DCT pyramid

Abstract: Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. In this paper, a face recognition system with low-memory requirement and accurate recognition is presented. It is based on extraction of features with the DCT pyramid, in contrast to the conventional method of wavelet decomposition. The DCT pyramid performed on each face image decomposes it into an approximation subband and the reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods. Moreover, it outperforms the other well known methods such as PCA and the block-based DCT with the zigzag scanning in terms of accuracy and memory requirement.

A robust digital image watermarking technique based on wavelet transform

Abstract: With the increasing use of internet and effortless copying, tempering and distribution of digital data, copyright protection for multimedia data has become an important issue. Digital watermarking emerged as a tool for protecting the multimedia data from copyright infringement. It is a technique for labeling multi-media data, including digital images, text documents, video and audio clips, by hiding secret information in the data. In this paper, we propose a digital image watermarking technique in the wavelet domain approach. It embeds the watermark in the components of the third band of the DWT of an image. Our experimental results prove that LL2 is the best region we can embed the watermark. The proposed technique is tested under Sharpen, Inverse, Gaussian, and Compress attacks to prove its robustness.

Introduction to the special issue on multimedia implementation », IEEE Trans. On Circuits and Systems for Video Technology

Abstract: Keywords: LTS3 Reference LTS-ARTICLE-2004-019 Record created on 2006-06-14, modified on 2016-08-08

A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR

Abstract: Machine tool selection has been an important issue in the manufacturing industry because improper machine tool selection can have a negative effect on productivity, accuracy, flexibility, and the responsive manufacturing capabilities of a company. The current multi-criteria decision making (MCDM) approach of machine tool selection mostly focuses on the subjective perspective. However, as the objective evaluation represents the actual performance of machine tools, both subjective and objective perspectives need to be considered when choosing an appropriate machining tool. Therefore, this study proposes a machine tool selection method based on a novel hybrid MCDM model. Firstly, the presented method employs a comprehensive weight technique integrating subjective weights obtained using fuzzy decision-making trial and evaluation laboratory (FDEMATEL) with objective weights obtained using entropy weighting (EW). Secondly, later defuzzification VIKOR (LDVIKOR) is put forward to rank the optional alternatives. Finally, a case application verifies the effectiveness of the proposed method. The evaluation results indicate that the best and worst selected machine tool of the proposed method keeps high conformance with the actual ranking in real factory. Additionally, sensitivity analysis results of the effect of parameters φ on the decision outcome show that irrespective of the variations in this parameter, the best decision outcome will be not influenced. These indicate that the presented hybrid model has advantages in granting flexibility to the preferences of decision makers.

Robust gait recognition: a comprehensive survey

Abstract: Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

Interactive Artificial Bee Colony Supported Passive Continuous Authentication System

Abstract: In this paper, an initiative passive continuous authentication (CA) system based on both hard and soft biometrics is presented. Human facial features are used as hard biometric information for the authentication process, and the clothes' color of a user is employed as the soft biometric information. The passive CA system keeps verifying, without interrupting the user from concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leaks, and denies access to the invader with the stolen account and password. In this system, the face recognition core is implemented not only by the Eigenface method, but also assisted by the interactive artificial bee colony optimization algorithm. The proposed method is evaluated by the ORL face database and tested on the prototype CA system for computer security. The experimental results indicate that the accuracy of recognition is raised up to 3.13%, i.e., from 83.75% to 86.88%, with data from the ORL database, and it is improved by 34.53% on average in the real-time continuous authentication environment.

A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement

Abstract: The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low–low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low–high (LH), high–low (HL), and high–high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.