Image-Based Vehicle Verification Using Steerable Gaussian Filter
Keywords:
Maximum Likelihood Estimation, Steerable Gaussian Filter, Supervised Classification, Vehicle Verification,Abstract
This paper presents a new feature descriptor for a vehicle verification system. The Steerable Gaussian Filter (SGF) is utilized to generate an image feature descriptor. The descriptor is constructed by concatenating the statistical parameters of the SGF filtered output. The Maximum Likelihood Estimation (MLE) estimates the statistical estimator using a heavy-tailed and bell-shaped distribution assumption such as Gaussian, Laplace, or Generalized Gaussian Distribution (GGD). A classifier assigns a class label of the vehicle hypothesis based on an image descriptor. As documented in the experimental results, the proposed feature descriptor achieves a promising result, and it outperforms the state-of-theart vehicle verification systems, making it a very competitive candidate in the practical applications.Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.