Image classification is a prerequisite for copy quality enhancement in all-in-one (AIO) device that comprises a
printer and scanner, and which can be used to scan, copy and print. Different processing pipelines are provided
in an AIO printer. Each of the processing pipelines is designed specifically for one type of input image to achieve
the optimal output image quality. A typical approach to this problem is to apply Support Vector Machine to
classify the input image and feed it to its corresponding processing pipeline. The online training SVM can help
users to improve the performance of classification as input images accumulate. At the same time, we want to
make quick decision on the input image to speed up the classification which means sometimes the AIO device
does not need to scan the entire image to make a final decision. These two constraints, online SVM and quick
decision, raise questions regarding: 1) what features are suitable for classification; 2) how we should control the
decision boundary in online SVM training. This paper will discuss the compatibility of online SVM and quick
decision capability.
Digital copiers are now widely used. One major issue for a digital copier is copy quality. In order to achieve as high quality as possible for every input document, multiple processing pipelines are included in a digital copier. Every processing pipeline is designed specifically for a certain class of document, which may be text, picture, or a mixture of both as is illustrated by the three examples shown in Fig. 1. In this paper, we describe an algorithm that can effectively classify an input image into its corresponding category. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.
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