A multiplication-free framework for signal processing and applications in biomedical image analysis

Date
2013
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1123 - 1127
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Series
Abstract

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE.

Course
Other identifiers
Book Title
Keywords
Absolute values, Biomedical image analysis, Cancer cell lines, Computationally efficient, Inner product, Product definition, Region covariance, Algorithms, Cell culture, Covariance matrix, Image classification, Signal processing, Vectors
Citation
Published Version (Please cite this version)