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This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost(More)
Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with(More)
Spiral Architecture is a relatively new and powerful approach to machine vision system. The geometrical arrangement of pixels on Spiral architecture can be described in terms of a hexagonal grid. However, all the existing hardware for capturing image and for displaying image are produced based on rectangular architecture. It has become a serious problem(More)
Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image(More)
A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear discriminant analysis (LDA) is applied to the set of key postures(More)
Fractal image compression is an efficient and effective technique in image coding. This paper presents a novel interactive progressive fractal decoding method, with which the compressed file can be transmitted incrementally and reconstructed progressively at users' side. It requires no modification to either encoder or decoder of any fractal image(More)
This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the(More)