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The interest in coding was very high because it is widely relied on in the security of correspondence and in the security of information in addition to the need to rely on it in the storage of data because it leads to a pressure in the volume of information when storing it. In this research, image transformation was used to encode gray or color images by adopting parameters elected from contourlet transformations for image. The color images are acquired into the algorithm, to be converted into three slices (the main colors of the image), to be disassembled into their coefficients through contourlet transformations and then some high frequencies in addition to the low frequency are elected in order to reconstruct the image again. The election of low frequencies with a small portion of the high frequencies has led to bury some unnecessary information from the image components. The performance efficiency of the proposed method was measured by MSE and PSNR criteria to see the extent of the discrepancy between the original image and the recovered image when adopting different degrees of disassembly level, in addition, the extent to which the image type affects the performance efficiency of the approved method has been studied. When the practical application of the method show that the level of disassembly is directly proportional to the amount of the error square MSE and also has a great effect on the extent of correlation where the recovered image away from the original image in direct proportional with the increased degree of disassembly of the image. It also shows the extent to which it is affected by the image of different types and varieties, where was the highest value of the PSNR (58.0393) in the natural images and the less valuable in x-ray images (56.9295) as shown in table 4.

With the rapid growth of computer usage to extract the required knowledge from a huge amount of information, such as a video file, significant attention has been brought towards multi-object detection and tracking. Artificial Neural Networks (ANNs) have shown outstanding performance in multi-object detection, especially the Faster R-CNN network. In this study, a new method is proposed for multi-object tracking based on descriptors generated by a neural network that is embedded in the Faster R-CNN. This embedding allows the proposed method to directly output a descriptor for each object detected by the Faster R-CNN, based on the features detected by the Faster R-CNN to detect the object. The use of these features allows the proposed method to output accurate values rapidly, as these features are already computed for the detection and have been able to provide outstanding performance in the detection stage. The descriptors that are collected from the proposed method are then clustered into a number of clusters equal to the number of objects detected in the first frame of the video. Then, for further frames, the number of clusters is increased until the distance between the centroid of the newly created cluster and the nearest centroid is less than the average distance among the centroids. Newly added clusters are considered for new objects, whereas older ones are kept in case the object reappears in the video. The proposed method is evaluated using the UA-DETRAC (University at Albany Detection and Tracking) dataset and has been able to achieve 64.8% MOTA and 83.6% MOTP, with a processing speed of 127.3 frames per second.

Biometric images are considered as one of the major coefficients in the field of personal authentication. One of the main approaches for personal identification is based on palm print. So studying the features extracted from palm print image adopted to get high efficient system for any recognition systems. In this research two major phases are hold on, in the first phase a database was built for 100 persons by acquiring four images for both hands (4 for left hand and 4 for right hand), then processed to extract ROI (region of interest) by looking for the palm centroid then a square area is fixed based on that centroid. The pre-process play an important step for stable features. Evaluation of the seven moments for each image (8 images) follow the preprocess then stored in the database file (so each person will have 56 values), this phase called personal database preparation. The second phase is the detection phase, which requires the same steps to get 56 values then go through the database looking for the closest person to the tested one. The system evaluation measured by statistical metrics which show good result goes up to 95.7% when applied on 50 persons with different conditions. Also the effect of ROI dimension with individual hands and integrated both of them studied, and the recommended dimension is 192*192.

Currently, there are many approaches for image transformations that are developed to cover the new technology in the huge amount of data treatment. In this paper, a study on Curvelet transformation coefficients was performed based on the aerial image to apply segmentation. This paper applies a lot of modifications on cut off frequencies on the filters, which is used to decompose the image on curvelet transformation. Two approaches are proposed and tested to look for the best segmentation result; the first one is based on designing filters manually, while the second evaluates the filter coefficients depending on the selected shape of the filters. The first technique gives acceptable segmentation and the second reaches the optimal result. One of the most important results is that the cut of frequency has a high effect on the segmentation; in addition, choosing filter parameters depended on the coefficients dimension of the curvelet transformation. Finally, the results show that the first approach underperformed the second one.

This research investigates the Hadith ("Were I to command one to prostrate to another, it would be wife to husband.",. This Hadith's tracks or sources and evidences are numerous, and they need to be carefully examined, especially as the Hadith has been a subject of talk by non-specialists, due to ignorance, arrogance, whims, or distortion purposes. The controversy has attracted arguments for and against the Hadith; some even claimed it to be inconsistent with the Qur'an despite its hypothetical condition. This necessitates Hadith specialist inquiry into it so that the truth of the matter can be available to all, and distortion attempts can be thwarted. The research aims to: gather and document all versions of the Hadith; to establish the reliability of the Hadith; and to refute whimsical allegations that are entertained by parties ignorant of related religious teachings. Methodologically, the research applies an inductive-deductive approach. It ends with thirtythree conclusions related to the Hadith chains of narrators, ranking, implications and linguistic analysis.

The noise have so many effects on secret message embedded on cover image, which cause a high distortion it could reach not to retrieve or difficult to recognize in case of image. In this research a study of noise effect on the image watermark when it embedded on the image contourlet coefficients (contourlet transformation was used as a recent transform for image), so the watermark was embedded in the coefficients which has the low energy to make less effect on the layout as much as possible in addition to give wide area. Different type of noise were added (Gaussain, Salt&Pepper and Speckle Noise) on the practical application shows a clear effect for Gaussain Noise and low effect for Speckle Noise in addition to high security robustness plus difficulty to attacked by hackers.

The main objective of speech coding is to allow the transmission of speech over digital channel of the highest speech quality and least possible bit rate, beside the security purpose. In this paper, the speech was coded by transforming it applying (which is often single dimension) into a two dimensional array to be suitable for transferring the countourlet transformation. Applied EZC (Embedded Zero tree Contourlet) algorithm, then is applied to the Huffman coding on the results of EZC, and used RLE (Run Length Encoding). The above idea gave the ability for coding, compression with retrieved information of high accuracy, using some measurements for quality measured of reconstructed signal, and found results show high similarity between the original and reconstructed signal.

Due to the fact that most of the application on EZT algorithm were applied on wavelet transformation. In the last ten years, the contourlet transformation shows that its efficiency is higher than the wavelet transformation due to its ability to deal with multidirections instead of the vertical and horizontal directions which are covered by the wavelet transformation. In this paper, the contourlet coefficient is adopted as inputs to the EZT (which normally are a wavelet coefficient ). Arranging the contourlet coefficient to be studied as input to EZT, the result of adopting modified contourlet coefficient was studied on two parameters (the file size and threshold value ) and tested by evaluating two factors (correlation and MSE). Comparing the result which we get it with the wavelet technique shows that the contourlet gives a result closer, to the original one depending on the correlation factor plus the PSNR factor. So, the proposed technique can be achieved.

In this research an algorithm was suggested for classifying a speaker age to two classes: (young and old classes) based on his speech signal. The suggested algorithm depend on a speech signal feature extraction in order to get a compact representation for this signal and to adopte these features in the classification process. In this algorithm, the eigen values of the covariance matrix was adopted as a principle parameter in the recognition between the two classes. It was constructed from the data of the speech signal (usually one dimension( after rearranghng it into a number (2,4,8,16,32,64) of a two dimension square matrix array . The suggested algorithm include two main stages: • 1st stage: includes data file preparation that contains the eigen values for a number of persons belong to both classes young and old (with different gender), in this stage the average of these values for each class to be calculated separately and the threshold curve(which represents the boundary seperating between two classes( were also computed. Second stage: in this stage the classification process was done by comparing the curve that represents the eigen values of the speech signal, with the threshold curve, a different number of performance parameters are adopted in the evaluation the accuracy of the classification process. The measured correlation value was in range of (0.9610, 0.9994) when m=2 and m=64, respectively (this means whenever the number of arrays that the speech signal constructed from it increases, the correlation coefficient also increases) , while a clear difference can be seen with mmse parameter. After applying the suggested algorithm on 50 persons from both genders, the algorithm passed in applying 80% and failed in percentage 20% of them.

Speech compression is very important in many applications. In this research we applied compression on speech by using wavelet transform and contourlet transform. By taking speech (which is often single dimension) into a two-dimensional array (to be suitable for transferring it to contourlet transformation), and then we applied wavelet transform .Then we make contourlet transform on coefficients of high wavelet. After transforming or storing speech and when using the decompression is applied by using inverse way of those transformations. Applied measuring (SNR, PSNR, NRMSE and Corr.) to test performance of the results, the results are very good.

In this paper eigenvalue of a gray image is used for image segmentation by calculating the eigenvalue for each segment and to be compared with a threshold value [which will be calculated dynamically by the system] and an iteration procedure will continue running until saturation arrived. By using the quadtree technique the gray image is divide into small segments depending on the value of the threshold and then the segmented image will pass into low pass filter in order to produce more enhancement in the image . The algorithm was applied on different types of images [natural images , text images] and both of them give high quality of segmentation .

In this research the covariance matrix which was used in so many fields, its eigen values adopted to be the main parameters for Latin printed character recognition. The idea was divided in two stages. The first stage is to generate the covariance matrix then, evaluate its eigen values to build main table for the whole latin characters in addition to the numeric values. The second stage is the recognition stage, which achieved to any character that was entered. The applied examples do not register any negative result. So it can be strongly recommended for printed character recognition.

In this paper an algorithm will be achieved to look for the properties of the skin for group then try to classify the skin of the group depending on the four properties (energy, contrast, correlation and homogeneity). Studying the four above properties in details then gave whole view about their effect on skin feature extraction. The applied algorithm shows that the four above properties can be extracted as features for personal skin. The experimental results of the proposed algorithm shows that the energy gave high recognition properties comparing with the remaining properties.

In this research, Normalized Cut algorithm was studied to segments any type of digital images which was widely used recently. In this paper wavelet transformation, and high-high frequency components which hold the edges of the image will be used to replace classical high frequency filters of the old techniques achieved by the normalized cut algorithm ( which used CANNY filter for edge detection). When the new modification of the proposed algorithm applied on the different type of digital images (medical, aerial and natural images) after crossover the CANNY filter by the wavelet high-high coefficients, a very efficient segments were found much better than the segments got before, in addition to a deep information can be seen if the selected segment is projected on the original image to be recognized.

This research aims to build a computer network system that works on detecting and locating flame by using fractal geometry in the digital video through building the network system in a server-client structure through which the characteristics of both the server and the clients are determined. A terminal stations were adapted to receive the digital video, and then divided into set of frames to be treated later on individually using fractal geometry (2 dimension variation algorithm to create fractal dimensions matrix). Studying and analyzing the value of the fractal dimensions of each frame, to identify the flame region by recognizing the common properties. Applying the suggested algorithm on Various samples exceeding 300 colored digital images containing flames with different spectra have been selected. Those images are taken by digital cameras or downloaded from the internet. On the other hand, the application on digital video applied and exceed 100 sample. Results show that using fractal dimension is an active and encouraging way in determining the characteristics of flame with its all spectra. No any negative evidence was recorded on all samples which exceed 100 sample from the digital video during the interval of the application test. Results also show the harmony and consistency in transferring process through the protocols that have been dealt with.

In this work the concepts of the pattern recognition was used to recognize printed Arabic characters, and the Fractal geometric dimension method was used. The input for the system is image, with bitmap format , then the image of character is recognized, and after that it is feeding to the OCR system. A feature space containing the values of the fractal dimension for the letters of Arabic was constructed. These features were used in the recognition phase. In this phase a comparison was made between the values in the feature space and the values of the letter inputs to be recognized, the comparison was done by the minimum Euclidian distance. Results of this work are 75% succeeded. And Matlab 6.5 is used to write the functions and subroutines for this work.

Pattern recognition, which takes features extraction as a basis for decision making, is considered as of the cutting-edge technologies. It is used in various useful applications such as tracking and monitoring objects. In this research an algorithm for detecting fire flame in the colored digital images is built. The algorithm basically depends an extracting the features of flame spots with all its spectra with reference to fractal dimension. The input image is cut into a set of equal dimension squares, then fractal dimension for each square is calculated using two-dimension variation algorithm, which is are of the algorithms used in calculating fractal dimension. Fractal dimension values in the output fractal dimension array are analyzed to detect flame spots using the computer through determining the common features and characteristics of the flame with all its spectra.

In this research a new algorithm was suggested for compressing speech files added a new style for storing signals, The suggested idea of compression begins with recording the speech via the microphone, then starting the proposed processing steps as follows : Removing silent period. Select the number of resulted signal samples. Segmenting the resulting signal to number of frames. Applying one of the curves fitting algorithms and obtaining the coefficients for the mathematical representation. Storing the results in a new file format with .ssc (Speech Signal Compression) extension. While the decompression process consisted of the reversal compression process steps, the signal is reconstructed using curve fitting coefficients which were stored in the new file, followed by returning the selected sample, then returning the silent period to their original location and finally listening to the retrieved speech signal. When the proposed algorithm had been applied on the files with different speech contents, the compression ratio was approximately (16.283%), and the ratio of SEGSNR was approximately (25.195dB).

In image processing, one of the main difficulties encountered in line detection is due to the various appearances of lines to be detected. This research presents a recognition method for line using Hough Transform (HT) .Applying erosion continuously to reach one pixel width of the generated skeleton for the image. Presented results are illustrating the utility of a new approach to know the content of an image, then store it using it's parameters and reconstruct it without the need to open it.

Abstract In this research, the algorithm of 2-Dimension variation was achieved, to extract a feature of the numerical number image. Because number recognition is very important subject in practical fields of image processing. The number will be segmented to its individual components (one image for each digit), Then preprocess will start by truncating all unused area from all the slides using the vertical and horizontal projection. The applied algorithm to compute the digit weight, can stated as follow:- - compute the distance geometry using 2-Dimension Variation algorithm which will be adopted as the digit feature. - Searching for the computed value in the data base (which was already build). - The weight of the digit will be computed depend upon the digit rank. - collecting all the weight to get the total weight of digit. When the algorithm applied on different numbers the result was with very low difference not more than 5 % when using numbers with single font and it rise little bit with numbers of mixed fonts.