Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
A Comparison of Gabor Filter Methods for Automatic Detection of Facial Landmarks
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Power line detection from optical images
Neurocomputing
| Hi-index | 0.00 |
In this paper we present a novel system for the detection and extraction of road map information from high-resolution satellite imagery. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (gabor filtering, tensor voting) and segmentation (graph-cuts) into a unified framework to address the problems of road feature detection and classification. Local orientation information is derived using a bank of gabor filters and is refined using tensor voting. A segmentation method based on global optimization by graph-cuts is developed for segmenting foreground(road pixels) and background objects while preserving oriented boundaries. Road centerlines are detected using pairs of gaussian-based filters and road network vector maps are finally extracted using a tracking algorithm. The proposed system works with a single or multiple images, and any available elevation information. User interaction is limited and is performed at the begining of the system execution. User intervention is allowed at any stage of the process to refine or edit the automatically generated results.