Randa Atta

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

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.

An efficient face recognition system based on embedded DCT pyramid

In this paper, an efficient feature selection method based on a combination of DCT pyramid for image decomposition and the concept of the set partitioning in hierarchal trees (SPIHT) for structuring of information for face recognition is presented. In the proposed method, the DCT pyramid decomposes each face image into an approximation subband and a set of reversed L-shape blocks containing the high frequency coefficients. The generalized parent-child relationships of SPIHT algorithm are then established among the DCT pyramids. This leads to efficient selection of the most important coefficients among the layers of the DCT pyramid. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based SPIHT feature selection. Moreover, it outperforms the other well-known methods such as the Eigenfaces and the block-based DCT with the zigzag scanning structure in terms of both accuracy and memory requirement.

An efficient eye detection and tracking system based on particle swarm optimization and adaptive block-matching search algorithm

Abstract The problem of eye detection and tracking in video sequences is very important for a large number of applications ranging from face recognition to gaze tracking. Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this paper, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms the deformable template matching based methods such as genetic and PSO. Moreover, it achieves better performance compared to model-based methods such as the statistical active appearance model (AAM) method and the edge projections based method in terms of accuracy and computational complexity.

Face recognition based on DCT pyramid feature extraction

Face recognition is a challenging problem due to variations in pose, illumination, and expression. Most techniques that can provide effective feature representation are based on wavelet transform. In this paper, an efficient feature extraction method based on DCT pyramid for face recognition is proposed. 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.

Human identification based on temporal lifting using 5/3 wavelet filters and radon transform

A spatio-temporal gait recognition system is proposed.A gait template approach (5/3GI) based on lifting 5/3 wavelet filters is introduced.Radon transform is performed on the generated temporal templates.PCA is utilized on Radon templates to reduce the dimensionality of feature vectors.The system achieves better performance compared with the recently published approaches. In this paper, a spatio-temporal gait recognition system is proposed to overcome the limitations associated with the most existing temporal template approaches such as gait energy image (GEI). These approaches do not preserve the whole temporal information in a gait sequence. They are also sensitive to changes in various conditions such as carrying and clothing. These limitations influence the performance of any gait recognition system. To address this problem, a temporal template approach based on lifting 5/3 wavelet filters is presented. In the proposed method named 5/3 gait image (5/3GI), the contour is first extracted from each image in a gait sequence. The gait contour images are then decomposed using 5/3 temporal wavelet filters into two temporal templates at the last temporal decomposition stage. These two templates are subjected to Radon transform for feature extraction. The principal component analysis (PCA) is subsequently applied to the Radon templates in the reference database to identify a subset of Radon template coefficients that carry the most important information suitable for gait recognition. Experimental results on USF HumanID and CASIA gait databases demonstrate that the proposed method achieves a better recognition performance than the most existing methods in the literature especially under walking variations.

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.

Low-Complexity Joint Temporal-Quality Scalability Rate Control for H.264/SVC

Rate control in scalable video coding (SVC) is a very challenging problem because of the inter-layer prediction structure, which makes developing an efficient rate-control algorithm complex and difficult. Little prior work is available for joint temporal-quality (T-Q) scalability considering the rate-distortion (R-D) dependence among the temporal and quality layers. However, most of the rate-control algorithms in SVC suffer from high computational complexity, growing significantly with the number of layers. In this paper, a single-pass joint temporal-quality rate-control algorithm is presented for H.264/SVC. In this algorithm, by analyzing the R-D dependence of joint T-Q scalability, Cauchy distribution-based rate-quantization, and distortion-quantization models, a set of empirical values are first derived to estimate the initial values of the R-D model parameters for the joint temporal and quality layers. Then, a novel prediction mechanism to update these model parameters is proposed to allocate the bit budgets efficiently among the temporal and quality layers, and hence to improve the performance of the proposed algorithm. Experimental results show that the proposed algorithm achieves better coding efficiency with low computational complexity compared with two other benchmark rate-control algorithms.