Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Detection and Modeling of Buildings from Multiple Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Extraction of Man-Made Objects from Aerial and Space Images
Automatic Extraction of Man-Made Objects from Aerial and Space Images
A three-dimensional color terrain modeling system for small autonomous helicopters
A three-dimensional color terrain modeling system for small autonomous helicopters
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Detection of Perceptual Junctions by Curve Partitioning and Grouping
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Real Time Aerial Natural Image Interpretation for Autonomous Ranger Drone Navigation
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Finite sample bias of robust scale estimators in computer vision problems
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
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In this paper, a new approach to finding and tracking various land cover boundaries such as rivers, agricultural fields, channels and roads for use in visual navigation system of an unmanned aerial vehicle is presented. We use a combination of statistical estimation and optimization techniques for extraction of dominant boundaries in noisy aerial images. A set of perceptual grouping restrictions is used to connect the acquired piecewise boundaries and to find the heading direction of the main boundary. The results are further refined by applying a set of texture and colour cues and eliminating any false hypothesis. To reduce the computation requirements, another approach based on sampled colour values of different land covers is also investigated. Colour characteristics of a set of manually selected windows are compared to select the best attributes needed for discrimination between different land covers in various (natural) lighting conditions. Each frame is then partially scanned and desired environmental features are extracted and classified. The results show that the proposed technique meets the minimum speed and accuracy requirement of aforementioned application and outperforms single-feature object tracking algorithms.