Unique Image Representation as a Tensor

<p>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.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2020

Author dc:creator

• Rosanlall, Bharat

Contributors dc:contributor

• Izidor Gertner

• Orthonormal Transform
• Tensor
• Coefficients
• Other Electrical and Computer Engineering

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/924

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1957