Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Robust Real-Time Face Detection
International Journal of Computer Vision
Real Time Aerial Natural Image Interpretation for Autonomous Ranger Drone Navigation
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Boosting: a classification method for remote sensing
International Journal of Remote Sensing
Aerial tracking of elongated objects in rural environments
Machine Vision and Applications
A Vision-Based Automatic Landing Method for Fixed-Wing UAVs
Journal of Intelligent and Robotic Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Robust 3D Face Recognition by Local Shape Difference Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust fully automatic scheme for general image segmentation
Digital Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Road Detection and Tracking from Aerial Desert Imagery
Journal of Intelligent and Robotic Systems
An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement
Journal of Intelligent and Robotic Systems
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
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In this paper we present a modular algorithm for interpretation of low altitude aerial images of non-urban environment. Non-urban land-covers, e.g., rivers, grass, unlike urban land-covers, have naturally unstructured boundaries and are usually containing diverse combination of colour and texture. The proposed method consists of a coarse and computationally efficient module, and a fine interpretation module. The coarse module is able to produce approximate estimations of land-covers using a single colour-base feature and contextual information. In cases when the coarse module fails, the fine module is able to accurately classify the desired land-cover. The fine module uses a combination of boundary, colour, texture and context features for accurate interpretation of the land-covers. The modular method inherits the high accuracy from the fine module and low computational expense from the coarse interpretation module. Experimental results show that the proposed algorithm can detect the target land-covers in low altitude aerial images of non-urban environment with acceptable accuracy and low computational requirements.