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Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.

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In this search, two methods were used to include the watermark in the video. The first method was based on DCT (Discrete Cosine Transform), the second method was based on an algorithm SVD (Singular Value Decomposition) for the purpose of converting video to frequency domain. The process of embedding the watermark in both methods was done after the original video was divided into a set of frames, and one frame was divided into a block of 8 x 8 and the DCT on each block when using the first method and the SVD algorithm when using the second method. And then include the Bit Binary for the watermark inside the center of the cluster. Random selection of video frames and rows of watermark images has been adopted in both ways. The performance of the two methods was assessed using the experimental tests PSNR, MSE and NC.The experimental results show that both methods have achieved a good understanding and high resistance against various attacks, adopted Matlab 2013a language.

tracking objects under the presence of noise, objects with partial and full occlusions in complex environments is a challenge for classical mean shift and unscented Kalman filter algorithms. In this paper we propose a new algorithm combining mean shift algorithm with corrected backgroundweighted histogram (CBWH) and unscented Kalman filter (UKF). The CBWH scheme can effectively reduce background's interference in target localization. So CBWH can guarantee accurate localization of the target. Then UKF algorithm has the ability to estimate the coming state. So the proposed algorithm is used to enhance the solution of object tracking problems. The experimental results show that the proposed method is superior to the traditional tracking methods.

Particle filter has grown to be a standard tool for solving visual tracking problems in real world applications. One of the critical tasks in object tracking is the tracking of fast moving objects in complex environments, which contain cluttered background and scale change. In this paper, a new tracking algorithm is presented by using the joint color texture histogram to represent a target and then applying it to particle filter algorithm called PFJCTH. The texture features of the object are extracted by using the local binary pattern (LBP) technique to represent the object. The proposed algorithm extracts effectively the edge and corner features in the target region, which characterize better and represent more robustly the target. The experiments showed that this new proposed algorithm produces excellent tracking results and outperforms other tracking algorithms.

The main purpose of this research is to give high accuracy result in pulmonary diseases diagnosis and attaining real medications that corresponds with the decisions of the pulmonary disease specialist. The neural network (perception network) which has ability of giving stable results in medical fields, was used for this purpose. Thirty samples were taken from infected patients with pulmonary diseases (Asthma, tuberculosis) and the network was trained of the symptoms of these diseases and samples. Good diagnostics results were attained corresponding with the symptoms of diseases. ‫ﺘﻤﻴﻴﺯ‬ ‫ﺍﻟﻌﺼﺒﻴﺔ‬ ‫ﺍﻟﺸﺒﻜﺎﺕ‬ ‫ﺒﺎﺴﺘﺨﺩﺍﻡ‬ ‫ﺍﻟﺼﺩﺭﻴﺔ‬ ‫ﺍﻷﻤﺭﺍﺽ‬ . ‫ﺍﺤﻤﺩ‬ ‫ﻓﺘﺤﻲ‬ ‫ﺇﻴﻤﺎﻥ‬ & ‫ﺇﺒﺭﺍﻫﻴﻡ‬ ‫ﺇﺴﻤﺎﻋﻴل‬ ‫ﻴﺤﻴﻰ‬