Çelik, Turgay (2011) A multiresolution framework for computer vision-based autonomous navigation. PhD thesis, University of Warwick.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Request Changes to record.

Abstract

Autonomous navigation, e.g., for mobile robots and vehicle driver assistance, rely
on intelligent processing of data acquired from different resources such as sensor
networks, laser scanners, and video cameras. Due to its low cost and easy installation, video cameras are the most feasible. Thus, there is a need for robust computer
vision algorithms for autonomous navigation.
This dissertation investigates the use of multiresolution image analysis and
proposes a framework for autonomous navigation. Multiresolution image representation is achieved via complex wavelet transform to benefit from its limited data
redundancy, approximately shift invariance and improved directionality. Image enhancement is developed to enhance image features for navigation and other applications. The colour constancy is developed to correct colour aberrations to utilize
colour information as a robust feature to identify drivable regions. A novel algorithm which combines multiscale edge information with contextual information
through colour similarity is developed for unsupervised image segmentation. The
texture analysis is accomplished through a novel multiresolution texture classifier.
Each component of the framework is initially evaluated independent of the
other components and on various and more general applications. The framework
as a whole is applied for drivable region identification and obstacle detection. The
drivable regions are identified using the colour information. The obstacle is defined
as vehicles on a road and other objects which cannot be part of a road. Multiresolution texture classifier and machine learning algorithms are applied to learn the
appearance of vehicles for the purpose of vehicle detection.

Item Type:	Thesis (PhD)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Library of Congress Subject Headings (LCSH):	Computer vision, Navigation -- Computer programs, Image analysis
Official Date:	January 2011
Dates:	Date Event January 2011 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Tjahjadi, Tardi
Extent:	xxiii, 203 leaves : ill., charts
Language:	eng

Request changes or add full text files to a record

Repository staff actions (login required)

View Item