A multiresolution framework for computer vision-based autonomous navigation

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A multiresolution framework for computer vision-based autonomous navigation

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