This research (The families of martyrs, victims of terrorism, war operations and military mistakes opinion about Al- Shuhada`a Establishment ) came to know and diagnose the mental image the martyrs victims of terrorism carries about the performance of Al- Shuhada`a Establishment and the services it provides to them, and to monitor the contents of that image that they had regarding their privileges and rights that Al- Shuhada`a Establishment supposed to give it to them, according to their law. The research problem was represented by the main question: (What is the image of Al- Shuhada`a Establishment among the families of martyrs, victims of terrorism, war operations and military mistakes), and this research was classified within the descriptive research, the two researchers adopted the survey method in order to achieve the objectives of the research, and they used the tool : the scale as tool for research, as the scale included several questions that were directed to the sample members to answer them. The two researchers chose the intentional sample of the martyrs’ families, the category of victims of terrorism, war operations and military mistakes among the other categories which was (200) single. The questionnaire was distributed to them and the results were extracted. The researchers reached several results, the most important of which were: 1- Most of the respondents (93.5%), confirmed that they did not get the privileges stipulated in the laws, similar to the segment of martyrs’ families of other groups. 2- Most of the respondents (92.5%) agreed that the establishment did not provide them with any job opportunities for those who do not have any job. 3- Most of the respondents (89.5%) answered that the institution does not respond to their questions and inquiries. 4- All respondents (100%) confirmed that the Foundation did not provide them with loans to set up their projects or build their homes. 5- Most of the respondents (96.5%) emphasized that the time to complete their transactions takes longer than it requires.
This paper presents a proposed method for (CBIR) from using Discrete Cosine Transform with Kekre Wavelet Transform (DCT/KWT), and Daubechies Wavelet Transform with Kekre Wavelet Transform (D4/KWT) to extract features for Distributed Database system where clients/server as a Star topology, client send the query image and server (which has the database) make all the work and then send the retrieval images to the client. A comparison between these two approaches: first DCT compare with DCT/KWT and second D4 compare with D4/KWT are made. The work experimented over the image database of 200 images of 4 categories and the performance of image retrieval with respect to two similarity measures namely Euclidian distance (ED) and sum of absolute diff
... Show MoreNowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. The major topic of this paper, is improve the compresses of still images by Multiwavelet based on estimation the high Multiwavelet coefficients in high frequencies sub band by interpolation instead of sending all Multiwavelet coefficients. When comparing the proposed approach with other compression methods Good result obtained
Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .
In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet
... Show MoreThe denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing. Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin
... Show MoreText 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.
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