The present research aims at revealing the advertising image semiotics in the American printed poster by following the image's significance and its transformations through the poster design trends and indicating its nature whether it is an explicit or implicit image. The limits of the research were the American printed poster during 2016-2018 period. The theoretical side was determined by two sections, the first: (the advertising image semiotics) and the second (design trends in the printed poster). The research procedures were represented by the research method adopted in the analysis of the sample models identified in four models taken from the research community which contains (24) models. The selection was made according to the trend and type of the mark and then analyzed according to a research tool whose paragraphs were bases taken from the theoretical framework. After analyzing the models that represent the research sample, the researcher reached at a set of results and conclusions and the most prominent of these were that ( the designers aimed at topics devoted in the collective memory such as the pigeon, olive branch, umbrella and freedom statute as part of the act of communication and the communication theory which the designer does not find difficult to convey), in accordance with the subject of his research in light of the focus on the advertising image based on the mark logic and his analysis of the semiotics of that image.
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.
This paper presents the matrix completion problem for image denoising. Three problems based on matrix norm are performing: Spectral norm minimization problem (SNP), Nuclear norm minimization problem (NNP), and Weighted nuclear norm minimization problem (WNNP). In general, images representing by a matrix this matrix contains the information of the image, some information is irrelevant or unfavorable, so to overcome this unwanted information in the image matrix, information completion is used to comperes the matrix and remove this unwanted information. The unwanted information is handled by defining {0,1}-operator under some threshold. Applying this operator on a given ma
... Show MoreImage 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
... Show MoreWe explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.
Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit
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