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In this paper, we propose a text extraction method from camera-captured document style images and propose a text segmentation method based on a color clustering method. The proposed extraction method detects text regions from the images using two low-level image features and verifies the regions through a high-level text stroke feature. The two level features are combined hierarchically. The low-level features are intensity variation and color variance. And, we use text strokes as a highlevel feature using multi-resolution wavelet transforms on local image areas. The stroke feature vector is an input to a SVM (Support Vector Machine) for verification, when needed. The proposed text segmentation method uses color clustering to the extracted text regions. We improved K-means clustering method and it selects K and initial seed values automatically. We tested the proposed methods with various document style images captured by three different cameras. We confirmed that the extraction rates are good enough to be used in real-life applications.

The goal of Semantic Web research is to transform the Web from a linked document repository into a distributed knowledge base and application platform, thus allowing the vast range of available information and services to be more effectively exploited. As a first step in this transformation, languages such as RDF and OWL have been developed; these languages are designed to capture the knowledge that will enable applications to better understand Web accessible resources, and to use them more intelligently. Although fully realising the Semantic Web still seems some way off, OWL has already been very successful, and has rapidly become a de facto standard for ontology development in fields as diverse as geography, geology, astronomy, agriculture, defence and the life sciences. An important factor in this success has been the availability of sophisticated tools with built in reasoning support. The use of OWL in large scale applications has brought with it new challenges, but recent research has shown how the OWL language and OWL tools can be extended and adapted to meet these challenges.

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This paper describes a new robot-photographer system which can interact with people. The goal of this research is to make the system act like a human photographer. This system is based on a mobile robot having capabilities of wireless communication and stereo vision. It recognizes waving hands of people, moves toward them, and takes pictures with designated compositions. The pictures are transmitted to personal computers over the wireless network. In comparison with previous researches, the most unique things of this system are human interaction and user preference. To realize these properties, this new robot photographer system is considered optical property of lens and applied interesting vision algorithm which was never tried before for previous robot photographer systems.