The Matching and Mosaic of the satellite imagery play an essential role in many remote sensing and image processing projects. These techniques must be required in a particular step in the project, such as remotely change detection applications and the study of large regions of interest. The matching and mosaic methods depend on many image parameters such as pixel values in the two or more images, projection system associated with the header files, and spatial resolutions, where many of these methods construct the matching and mosaic manually. In this research, georeference techniques were used to overcome the image matching task in semi automotive method. The decision about the quality of the technique can be considered if the error value is less than half a pixel. The projection-based method was used to ensure the mosaic process. The test images are satellite imagery with medium spatial resolutions; these images were processed to ensure the results. In matching techniques, the different sensor images (different in resolutions) were investigated using image resize and sampling. The results were obtained using many remote sensing packages and written programs in Matlab environmental.
In some cases, surgeons need to navigate through the computer system for reconfirmation patients’ details and unfortunately surgeons unable to manage both computer system and operation at the same time. In this paper we propose a solution for this problem especially designed for heart surgeon, by introducing voice activation system with 3D visualization of Angiographic images, 2D visualization of Echocardiography processed video and selected patient’s details. In this study, the processing, approximation of the 3D angiography and the visualization of the 2D echocardiography video with voice recognition control are the most challenging work. The work involve with predicting 3D coronary three from 2D angiography image and also image enhan
... Show MoreIn this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo
... Show MoreThis study is one of the descriptive studies, where the researcher used the survey method, which seeks to provide an accurate and appropriate description of the main and sub-categories of the content of the research for television documentaries on the Iraqi satellite channel, relying on the research questions and objectives to analyze the content of the documentaries according to the approach referred to above. The objectives of the research were summarized to identify the function of the documentary programs in Al-Iraqiya channel to detect the crimes of the former regime, to know the frameworks highlighted by the documentary programs in Al-Iraqiya channel to reveal the crimes of the former regime, to identify the extent of the circ
... Show MoreThe combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.
In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.
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... Show MoreThis paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4
... Show MoreThe problem of rapid population growth is one of the main problems effecting countries of the world the reason for this the growth in different environment areas of life commercial, industrial, social, food and educational. Therefore, this study was conducted on the amount of potable water consumed using two models of the two satellite and aerial images of the Kadhimiya District-block 427 and Al-Shu,laa district-block 450 in Baghdad city for available years in the Secretariat of Baghdad (2005, 2011,2013,2015). Through the characteristics of geographic information systems, which revealed the spatial patterns of urban creep by determining the role and buildings to be created, which appear in the picture for the
... Show MoreIn this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between ev
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