Madhur Srivastava

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In this paper, we carry out a comparative study of the efficacy of wavelets belonging to Daubechies and Coiflet family in achieving image segmentation through a fast statistical algorithm.The fact that wavelets belonging to Daubechies family optimally capture the polynomial trends and those of Coiflet family satisfy mini-max condition, makes this comparison(More)
An algorithm is proposed for the segmentation of image into multiple levels using mean and standard deviation in the wavelet domain. The procedure provides for variable size segmentation with bigger block size around the mean, and having smaller blocks at the ends of histogram plot of each horizontal, vertical and diagonal components, while for the(More)
We explicate a semi-automated statistical algorithm for object identification and segregation in both gray scale and color images. The algorithm makes optimal use of the observation that definite objects in an image are typically represented by pixel values having narrow Gaussian distributions about characteristic mean values. Furthermore, for visually(More)
The paper introduces the idea of non-uniform quantization in the detail components of wavelet transformed image. It argues that most of the coefficients of horizontal, vertical and diagonal components lie near to zeros and the coefficients representing large differences are few at the extreme ends of histogram. Therefore, this paper advocates need for(More)
—In this paper, we have proposed a novel method of image representation on quantum computers. The proposed method uses the two-dimensional quantum states to locate each pixel in an image matrix with the normalized amplitude values of the image signal being coefficients of the quantum states. The two-dimensional quantum states are linear superposition of(More)
—The paper presents a non-linear quantization method for detail components in the JPEG2000 standard. The quantization step sizes are determined by actual statistics of the wavelet coefficients. Mean and standard deviation are the two statistical parameters used to obtain the step sizes. Moreover, weighted mean of the coefficients lying within the step size(More)