Color models for outdoor machine vision
Computer Vision and Image Understanding
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
A static images based-system for traffic signs detection
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Fuzzy-based algorithm for color recognition of license plates
Pattern Recognition Letters
Shadows attenuation for robust object recognition
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A convenient feature vector construction for vehicle color recognition
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Relative color polygons for object detection and recognition
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Task-oriented navigation algorithms for an outdoor environment with colored borders and obstacles
Intelligent Service Robotics
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The color associated with an object in machine vision images is not constant; under varying illuminating and viewing conditions (such as in outdoor images), the perceived color of an object can vary significantly, thus making color-based recognition difficult. Existing methods in color-based recognition have been applied mostly to indoor and/or constrained imagery, but not to realistic outdoor data.This work analyzes the variation of object color in outdoor images with respect to existing models of day-light illumination and surface reflectance. Two approaches for color recognition are then proposed: the first develops context-based models of daylight illumination and hybrid surface reflectance, and predicts the color of objects based on scene context. The secondmethod shows that object color can be nonparametrically "learned" through classification methods such as Neural Networks and Multivariate Decision Trees. The methods have been successfully tested in domains such as road/highway scenes, off-road navigation and military target detection.