Digital water marking technique suffered some problem of geometrical and some other attack. The process of attack deformed the quality of digital image and violet the rule of copyright protection low. For the roughness of digital image watermarking used wavelet transform function and RBF neural network. The RBF neural network trained the pattern of digital image pixel and finally embedded the image. The processes of validation of blindness of digital image apply some geometrical attack. Our empirical result computed in form of PSNR value and number of correlation of embedded image. Our evolution process shows better result in compression of transform based digital water marking technique.
Human detection plays an important role in security surveillance and computer vision. The process of human detection is very complex due to variant feature of human such as color, texture and shape and size. The process of feature extraction imparts a major role in human detection technique. Now a days used classification technique to define the feature of human. The classification process define the pattern of feature for the process of detection, the process of features generates a bag of feature for the process of classification technique. In this paper improved the support vector machine classification technique for the classification of human detection. The improved support vector machine is called cascaded support vector machine. The cascading of support vector machine improved the process of human detection. Our proposed algorithm implemented in MATLAB 7.8.0 software and used human video of different location. Our empirical evaluation of experimental result shows that the proposed methods give a better result in compression of support vector machine classifier.
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