This study deals with the biostratigraphy of Shiranish Formation (Late Cretaceous), depending on the Ammonite and associated Foraminifera in four outcrop sections, three of which are located in Al-Sulaimaniya governorate  (Dokan, Esewa and Kanny dirka sections) and one in Erbil  governorate, northern Iraq (Hijran section). Fourteen species of Ammonite belonging to fourteen genera were determined, which are: Dsemoceratidae, Gaudryceras, Gunnarites, Hoplitoplacenticeras, Kitchinites, Kossmaticeratinae, Neancyloceras, Neokossmaticeras, Nostoceras, Paratexanites, Partschiceras, Phylloceras, Pseudophyllites and Yubariceras. Also, thirty- five species of Foraminifera belonging to thirteen genera w

The present study deals with some morphological and anatomical characteristics of the Nonea echioides(L.) Roem. & Sehult species belonging to Boraginaceae, which is recorded to have spread recently in Kurdistan region of Iraq.

This research focused on some of the important morphological characteristics of the stems, leaves, flowers, and fruits and comparing them with other studies of neighboring countries to Iraq. These morphological characteristics were found to be important in isolating the species of the filed. The anatomical features of the epidermis, stomata, and trichomes were also investigated. The study shows that Nonea echioides belongs to C₃ plants based on the anatomical featur

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

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

Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform w

Due to the vast using of digital images and the fast evolution in computer science and especially the using of images in the social network.This lead to focus on securing these images and protect it against attackers, many techniques are proposed to achieve this goal. In this paper we proposed a new chaotic method to enhance AES (Advanced Encryption Standards) by eliminating Mix-Columns transformation to reduce time consuming and using palmprint biometric and Lorenz chaotic system to enhance authentication and security of the image, by using chaotic system that adds more sensitivity to the encryption system and authentication for the system.