Probabilistic modeling and fusion for image feature extraction with applications to license plate detection

The paper proposes a novel feature fusion concept for object extraction. The image feature extraction process is modeled as a feature detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the final features for further processing. Along with a probabilistic model, pixels voting algorithm is also tested through binary threshold variation. The performance of these approaches is compared with the traditional approaches of image feature extraction in the context of automatic license plate detection problem.