Inference of Curvilinear Structure based on Learning a Ranking Function and Graph Theory

To detect curvilinear structures in natural images, we propose a novel rankinglearning system and an abstract curvilinear shape inference algorithm based on graph theory. Weanalyze the curvilinear structures as a set of small line segments. In this work, the rankings ofthe line segments are exploited to systematize the topological feature of the curvilinear structures.Structured Support Vector Machine is employed to learn the ranking function that predicts thecorrespondence of the given line segments and the latent curvilinear structures. We first extractcurvilinear features using morphological profiles and steerable filtering responses. Also, we proposean orientation-aware feature descriptor and a feature grouping operator to improve the structuralintegrity during the learning process. To infer the curvilinear structure, we build a graph based onthe output rankings of the line segments. We progressively reconstruct the curvilinear structureby looking for paths between remote vertices in the graph. Experimental results show that theproposed algorithm faithfully detects the curvilinear structures within various datasets

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability