International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
LADAR target detection using morphological shared-weighted neural networks
Machine Vision and Applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Vehicle Detection on Aerial Images: A Structural Approach
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Vehicle Ground-Truth Database for the Vertical-View Ft. Hood Imagery
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Nonlinear correlation filter and morphology neural networks for image pattern and automatic target recognition
Fast curvilinear structure extraction and delineation using density estimation
Computer Vision and Image Understanding
Computers and Electrical Engineering
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High-resolution satellite imagery has recently become a new data source for extraction of small-scale objects such as vehicles. Very little vehicle detection research has been done using high-resolution satellite imagery where panchromatic band resolutions are presently in the range of 0.6-1.0m. Given the limited spatial resolution, reliable vehicle detection can only be achieved by incorporating contextual information. Here, a GIS road vector map is used to constrain a vehicle detection system to road networks. We used a morphological shared-weight neural network (MSNN) to learn an implicit vehicle model and classify pixels into vehicles and non-vehicles. A vehicle image base library was built by collecting more than 300 cars manually from test images. Strategies to reduce the false alarms and select target centroids were designed. Experimental results indicate that the MSNN performed very well. The detection rate on both training and validation sites exceeded 85% with very few false alarms. By learning the implicit vehicle model through a MSNN, our method outperforms a baseline blob detection method.