Unique Image Representation as a Tensor
type: CreativeWork
description: 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.
abstract: 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.
author: Rosanlall, Bharat
keywords: Orthonormal Transform, Tensor, Coefficients, Other Electrical and Computer Engineering
date published: 2020-01-01T08:00:00Z
date modified: 2026-07-24T01:57:28Z
provider: City University of New York - City College
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: City University of New York - City College