A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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This paper describes a method of image processing for the 2D bar code image recognition, which is capable of processing images extremely rapidly and achieving high recognition rate. This method includes three steps. The first step is to find out the four vertexes of ROI (Regions Of Interest); the second is a geometric transform to form an upright image of ROI; the third is to restore a bilevel image of the upright image. This work is distinguished by a key contribution, which is used to find the four vertexes of ROI by using an integrated feature. The integrated feature is composed of simple rectangle features, which are selected by the AdaBoost algorithm. To calculate these simple rectangle features rapidly, the image representation called "Integral Image" is used.