This thesis presents a two dimensional orthonormal transform that represents an image as coefficients in 4 independent channels. The salient feature of these coefficients is that they contain complete position spatial frequency information about the image, in a sense that the original image can be reconstructed from these coefficients with negligible error. These coefficients can be used in various machine learning, AI , and other tasks where data features are used. Popular convolutional layer used in various neural networks reduces information and can not reconstruct original image. In this thesis , we present several examples where these coefficients are used in image classification tasks for a standard data set.
Orthonormal Transform, Tensor, Coefficients, Other Electrical and Computer Engineering
City University of New York - City College
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