Median filter is adopted to match the noise statistics of the degradation seeking good quality smoothing images. Two methods are suggested in this paper(Pentagonal-Hexagonal mask and Scan Window Mask), the study involved modified median filter for improving noise suppression, the modification is considered toward more reliable results. Modification median filter (Pentagonal-Hexagonal mask) was found gave better results (qualitatively and quantitatively ) than classical median filters and another suggested method (Scan Window Mask), but this will be on the account of the time required. But sometimes when the noise is line type the cross 3x3 filter preferred to another one Pentagonal-Hexagonal with few variation. Scan Window Mask gave bett

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.

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

Lowpass spatial filters are adopted to match the noise statistics of the degradation seeking
good quality smoothed images. This study imply different size and shape of smoothing
windows. The study shows that using a window square frame shape gives good quality
smoothing and at the same time preserving a certain level of high frequency components in
comparsion with standard smoothing filters.

Context and its influence in direction of reference by Abi Hussein Al Basri

The current research aims to identify: 1. The level of mathematical construct among the Department of Mathematics students in the colleges of education and basic education. 2. The level of effective mathematical operations in both sides of the brain at the Department of Mathematics students in the colleges of education and basic education. 3. The strength and direction of the correlation between the mathematical construct and effective mathematical operations on both sides of the brain at the Department of Mathematics students in the colleges of Education and Basic Education. To investigate the research objectives, the researcher formulated zero-main hypothesis for each aim and from the same hypothesis, three sub-zero hypotheses are deri

   The subject of the organizational Ambidexterity of the vital Topics through which it seeks organizations to provide mentalities renewable for their members and maintain its survival and continuity according to different organizational methods of access for strategic success.     The research aims to demonstrate the impact of organizational Ambidexterity in achieving strategic success in the National bank of Iraq, and the questionnaire was prepared as a tool for collecting data and information through sample  survey of (16) managers and heads of departments , The results were analyzed by using the statistical program (SPSS) in calculating  mean, standard deviation, percent and test (f), coefficie

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.