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Novel DWT Video Watermarking Schema

Ahmed K., El-Henawy I., Atwan A.

Machine Graphics and Vision

2009

Vol. 18, No. 3

363-380

In this paper a new video watermarking scheme is proposed which depends on 2-level Discrete Wavelet Transform decomposition of each component of an RGB video frame. The scheme embeds independent watermarks into different shots. A genetic algorithm is employed to match shots to watermarks. The scheme chooses between the HLi of red or green or blue components of each frame based on a key and embeds error correcting code into one of them. The scheme is blind. Experimental results show that the scheme is robust against attacks such as frame dropping, frame averaging, frame swapping, statistical analysis, and MPEG-2 and MPEG-4 compression. The proposed scheme uses a composite three-element key to increase the security.

On wavelets applications in medical image denoising

El-Henawy I., El-Areef T., Karawia A. A.

Machine Graphics and Vision

2003

Vol. 12, No. 3

393-404

An important application domain of the wavelet theory is denoising. In this paper, we use the wavelet transforms to denoise the medical images. There are many kinds of noise and we study only three types; i) additive random noise; ii) pop noise and; iii) localized random noise. Further, we use Root Mean Square Error(RMSE) and Signal to Noise Ratio (SNR) to measure the error between a noisy image and the original image.

On wavelets applications in image compression

El-Henawy I., Karawia A.

Machine Graphics and Vision

2003

Vol. 12, No. 4

515-524

An important application domain of the wavelet theory is compression. In this paper, we use wavelet transforms to compress two different types of images (i) medical images (Echo image), (ii) color images, by using two different procedures. We use different types of wavelet transforms. Further, the compression ratio, the bits per pixel and the relative 2-norm difference are calculated. The quantitative measures are used to compare and contrast the performance of different wavelet transforms.

A neurocomputing approach to the correspondence problem in stereo vision based upon an unsupervised neural network

El-Henawy I., Fouda Y. M., Enab Y. M.

Machine Graphics and Vision

2001

Vol. 10, No. 1

29-46

The stereo matching problem is one of the most widely stidied problems in stereo vision. In this paper we introduce a neurocomputing approach to the local stereo matching problem using edge segments as features with several attributes. Most classical local stereo matching techniques use features representing objects in both images and compute the minimum values of attribute differences. pajares et al ([21]) had verified that the differences in attributes, for the true matches, cluster in a cloud around a center. We used the self-organizing neural network to get the best cluster center. Based on the similarity constraint, we compute the minimum Mahalaobis distances between the differences of the attributes for a new pair of features and the cluster center to classify this new pair as true or false match. Experimental results with two real pairs of images are shown.