Mohiy M. Hadhoud

An Efficient Block-by-Block SVD-Based Image Watermarking Scheme

This paper presents a block based digital image watermarking that is dependent on the mathematical technique of singular value decomposition (SVD). Traditional SVD watermarking already exists for watermark embedding on the image as a whole. In the proposed approach, the original image is divided into blocks, and then the watermark is embedded in the singular values (SVs) of each block separately. This segmentation process and watermarking on a block-by-block basis makes the watermark more robust to the attacks such as noise, compression, cropping and other attacks as the results reveal. Watermark detection is implemented by extracting the watermark from the SVs of the watermarked blocks. Extracting the watermark from one block at least is enough to ensure the existence of the watermark.

Homomorphic image watermarking with a singular value decomposition algorithm

Information embedding and retrieval in digital images.Digital Watermarking.Image processing. In this paper, a new homomorphic image watermarking method implementing the Singular Value Decomposition (SVD) algorithm is presented. The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform. The reflectance component contains most of the image features but with low energy, and hence watermarks embedded in this component will be invisible. A block-by-block implementation of the proposed method is also introduced. The watermark embedding on a block-by-block basis makes the watermark more robust to attacks. A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks. The proposed method is more robust to various attacks. The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security.

Chaotic image encryption in transform domains

This paper presents a new approach for image encryption based on the chaotic Baker map, but in the different transform domains. The discrete cosine transform (DCT), the discrete sine transform (DST), the discrete wavelet transform (DWT) and the additive wavelet transform (AWT) are exploited in the proposed encryption approach. Chaotic encryption is performed in these transform domains to make use of the characteristics of each domain. A comparison study between all transform domain encryption schemes is held in the presence of different attacks. Results of this comparison study are in favor of encryption in the DST domain if the degree of randomness is of major concern.

Arabic character recognition using statistical and geometric moment features

Feature extraction is one of the most important steps in character recognition system as each character has different features that distinguish it from other characters. Choosing good features is an important factor that affects recognition process. This paper proposed a novel and effective procedure for recognizing Arabic characters using a combination of statistical features and geometric moment features which are independent of the font and size of the character. These features are used by backpropagation neural network to classify the characters. Recognition rate of 97% is achieved using 6 different fonts.

Digital images inpainting using modified convolution based method

Reconstruction of missing parts or scratches of digital images is an important field used extensively in artwork restoration. This restoration can be done by using two approaches, image inpainting and texture synthesis. There are many techniques for the two pervious approaches that can carry out the process optimally and accurately. In this paper the advantages and disadvantages of most algorithms of the image inpainting approach are discussed. The modification to Oliveira inpainting model is introduced. This modification produces fast and good quality with one iteration without blur and removes large object with symmetric background.

Efficient implementation of chaotic image encryption in transform domains

Investigation of transform domain encryption.IWT encryption study.Study of chaotic Baker map permutation. The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.

Fully automatic liver tumor segmentation from abdominal CT scans

Liver cancer causes the majority of primary malignant liver tumors among adults. Computed Tomography (CT) scans are generally used to make the treatment plan or to prepare for ablation surgery. Processing CT image includes the automatic diagnosis of liver pathologies, such as detecting lesions and following vessels ramification, and 3D volume rendering. This paper presents a new fully automatic method to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some pepper noise and tumors as dark gray spots. After applying Gaussian smoothing, Isodata threshold is used to turn the image into binary with tumors as black spots on white background. Tests are reported on abdominal datasets showing promising result.

Texture Recognition Based Natural Gray Images Coloring Technique

In this paper, we propose a new technique for computer coloring gray scale images. This technique works for texture based images like natural scenes. Its based on segmenting the image into different regions according to their textures and then classifying these textures to predefined texture classes to get their real colors. Recognition of these textures is performed by matching these textures with a training set stored in a special database. Most of authors working in coloring of gray scale images have used primitive methods for coloring which are both inaccurate and limited. At the end of this paper we show that our coloring system has succeeded to colorize the natural gray images with real colors in high quality.

An efficient SVD image steganographic approach

this paper presents an efficient SVD based image Steganography approach. This approach aims at increasing the fidelity of the images after data embedding. Rather than embedding data in the singular values of the images, the image Left singular vectors are used for this purpose. The objective of this approach is to reduce the embedding errors as well as to maintain the image fidelity. Several solutions to the image fidelity problem are presented in the paper. Experimental results show the superiority of the proposed Steganography approach to the existing approaches.

A novel fully automatic technique for liver tumor segmentation from CT scans with knowledge-based constraints

The liver is a common site for the occurrence of tumors. Automatic hepatic lesion segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some pepper noise and tumors as dark gray spots. After applying Gaussian smoothing, Isodata is used to threshold the tumor in the slice. In order to eliminate erroneous segmentation a discriminative rule based on diagnostic knowledge on liver cancer shape is applied. Finally, a 3-D consistency check is performed based on three-dimensional information that a lesion mass cannot appear in a single slice independently. Tests are performed on abdominal datasets showing promising result. Using MICCAI 2008 segmentation evaluation metrics, the novel proposed technique achieved 80.19 as a total score.