Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurre

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

Dans le roman moderne, le type du héros est depuis longtemps devenu suspect.  Il risque même de disparaitre dans ce qu´on désigne le Nouveau Roman qui, se concentrant plutôt sur les objets, décrits minutieusement, refuse la fonction épistémologique traditionnelle de la littérature. Cette conception se manifeste, sur le plan formel, par certains traits typiques, comme la relativisation des points de vue, la décomposition de l´action, la destruction du temps, la décomposition de l´espace et la désintégration du personnage romanesque dont les liens avec la société sont coupés.

تُعبّرُ الصُّورةُ الحسَّيةُ في شعرِ ابن دُنَينير الموصليِّ^([i]) في بنيتها عن تجربةِ الشاعرِ الوجدانيةِ والذهنيةِ, وأفكارهِ ومشاعرِهِ؛ فيصوغُ بها مَفهومًا جديدًا للواقعِ الماديِّ والمعنويِّ، الذي يتسمُ بالوضوحِ أولاً، وبالقرْبِ من الذهنِ ثانيًا، للربْطِ بين الحواسِّ الإنسانيةِ والمعاني الذهنيةِ، لِتُقَدِّمَ الصُّورةُ الحسيَّةُ إلى ((المتلقي صُورًا مرئيةً، يُعادُ تشكيلُها سياق

Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and the
computational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.
The results showed a significant reduct

     Recognizing cars is a highly difficult task due to the wide variety in the appearance of cars from the same car manufacturer. Therefore, the car logo is the most prominent indicator of the car manufacturer. The captured logo image suffers from several problems, such as a complex background, differences in size and shape, the appearance of noise, and lighting circumstances. To solve these problems, this paper presents an effective technique for extracting and recognizing a logo that identifies a car. Our proposed method includes four stages: First, we apply the k-medoids clustering method to extract the logo and remove the background and noise. Secondly, the logo image is converted to grayscale and also converted to a binary imag

This article presents a polynomial-based image compression scheme, which consists of using the color model (YUV) to represent color contents and using two-dimensional polynomial coding (first-order) with variable block size according to correlation between neighbor pixels. The residual part of the polynomial for all bands is analyzed into two parts, most important (big) part, and least important (small) parts. Due to the significant subjective importance of the big group; lossless compression (based on Run-Length spatial coding) is used to represent it. Furthermore, a lossy compression system scheme is utilized to approximately represent the small group; it is based on an error-limited adaptive coding system and using the transform codin

Examining and comparing the image quality of degenerative cervical spine diseases through the application of three MRI sequences; the Two-Dimension T2 Weighed Turbo Spin Echo (2D T2W TSE), the Three-Dimension T2 Weighted Turbo Spin Echo (3D T2W TSE), and the T2 Turbo Field Echo (T2_TFE). Thirty-three patients who were diagnosed as having degenerative cervical spine diseases were involved in this study. Their age range was 40-60 years old. The images were produced via a 1.5 Tesla MRI device using (2D T2W TSE, 3D T2W TSE, and T2_TFE) sequences in the sagittal plane. The image quality was examined by objective and subjective assessments. The MRI image characteristics of the cervical spines (C4-C5, C5-C6, C6-C7) showed significant difference

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signa