Tracking and data association
Data association methods for tracking systems
Active vision
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Model Based Tracking for Navigation and Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Modelling of single mode distributions of colour data using directional statistics
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Sequential probabilistic grass field segmentation of soccer video images
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
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An experimental vehicle is being developed for the purposes of precise crop treatment, with the aim of reducing chemical use and thereby improving quality and reducing both costs and environmental contamination. For differential treatment of crop and weed, the vehicle must discriminate between crop, weed and soil. We present a two stage algorithm designed for this purpose, and use this algorithm to illustrate how empirical discrepancy methods, notably the analysis of type I and type II statistical errors and receiver operating characteristic curves, may be used to compare algorithm performance over a set of test images which represent typical working conditions for the vehicle. Analysis of performance is presented for the two stages of the algorithm separately, and also for the combined algorithm. This analysis allows us to understand the effects of various types of misclassification error on the overall algorithm performance, and as such is a valuable methodology for computer vision engineers.