The growing use of tele

This paper presents a new secret diffusion scheme called Round Key Permutation (RKP) based on the nonlinear, dynamic and pseudorandom permutation for encrypting images by block, since images are considered particular data because of their size and their information, which are two-dimensional nature and characterized by high redundancy and strong correlation. Firstly, the permutation table is calculated according to the master key and sub-keys. Secondly, scrambling pixels for each block to be encrypted will be done according the permutation table. Thereafter the AES encryption algorithm is used in the proposed cryptosystem by replacing the linear permutation of ShiftRows step with the nonlinear and secret pe

     Today in the digital realm, where images constitute the massive resource of the social media base but unfortunately suffer from two issues of size and transmission, compression is the ideal solution. Pixel base techniques are one of the modern spatially optimized modeling techniques of deterministic and probabilistic bases that imply mean, index, and residual. This paper introduces adaptive pixel-based coding techniques for the probabilistic part of a lossy scheme by incorporating the MMSA of the C321 base along with the utilization of the deterministic part losslessly. The tested results achieved higher size reduction performance compared to the traditional pixel-based techniques and the standard JPEG by about 40% and 50%,

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

سها علي حسين, هويدة إسماعيل إبراهيم, Journal of Physical Education, 2017 - Cited by 1

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.