This paper describes a method for the classification of bank notes. The algorithm has three stages, and classifies bank notes with v ery low error rates and at high speeds.To achiev e the v ery low error rates, the result of classification is check ed in the final stage by using different features to those used in the first two. High-speed processing is mainly achiev ed by the hierarchical structure, which leads to low computational costs. I n ev aluation on 32, 850 samples of US bank notes, with the same number used for training, the algorithm classified all samples precisely with no error sample.
ment 1 because the characters in the set were used to train the classifier and, therefore, the similarity worked well in selecting the correct segmentation hypothesis without the penalties of peripheral features. When contextual segmentation was used, the difference between the performance with and without the developed method was very small in effect. However, there still exist several cases where the similarity is not reliable because the quality of the image is very bad and the contextual segmentation algorithm cannot judge which candidate word is best, even though the correct word is included in the candidates. Especially in such a case, the peripheral feature analysis is effective to judge which is a better candidate.
This paper presents a visual odometer system using a monocular camera for vehicle navigation. A novel algorithm for vehicle ego-motion estimation based on optical flow and image segmentation is proposed. By applying a Pulse-Coupled Neural Network (PCNN), the image is dynamically divided into road area and non-road area by analysing texture smoothness. Correct road region detection effectively reduces computation cost and improves accuracy of egomotion estimation. Then a novel optical flow optimization method is proposed to produce reliable optical flow field in the road area detected previously. It's known when the vehicle is moving on a planar structured road, its 2-D motion field is expected to have specific form. Therefore ego-motion of vehicle, instantaneous speed and angular velocity, can be recovered from optical flow field of road area. Experiments show that the visual odometer successfully provides driver with robust and accurate vehicle self motion information.
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