Resolution Mosaic Image Segmentation
This event took place on Wednesday 09 July 2008 at 11:30
Mohammed Abdel-Megeed Salem Humboldt-Universitaet zu Berlin
Due to the popularity of visual-based information in the forms of images and videos, more computer vision systems take part in the automation of diverse applications, such as medical diagnosing, monitoring and security. Segmentation is a necessary step in almost every pattern recognition and computer vision system. As the spectrum of the applications expands, the demand for an accurate and fast segmentation process increases.
In this talk we present an algorithm for image segmentation, that is based on the multiresolution analysis and the well known expectation maximisation algorithm. Based on the distribution of the information contained in the image, the multiresolution analysis is used to re-represent the image in a mosaic of different resolutions. Within this new re-representation the irrelevant information is suppressed in the segmentation process. The image is then modelled to be generated by a statistical field. The expectation maximisation algorithm is used to find the missing parameters of the model. A magnetic resonance image and many synthetic images were used for testing and evaluating the algorithm. The experiments show its robustness against noise.
This event took place on Wednesday 09 July 2008 at 11:30
Due to the popularity of visual-based information in the forms of images and videos, more computer vision systems take part in the automation of diverse applications, such as medical diagnosing, monitoring and security. Segmentation is a necessary step in almost every pattern recognition and computer vision system. As the spectrum of the applications expands, the demand for an accurate and fast segmentation process increases.
In this talk we present an algorithm for image segmentation, that is based on the multiresolution analysis and the well known expectation maximisation algorithm. Based on the distribution of the information contained in the image, the multiresolution analysis is used to re-represent the image in a mosaic of different resolutions. Within this new re-representation the irrelevant information is suppressed in the segmentation process. The image is then modelled to be generated by a statistical field. The expectation maximisation algorithm is used to find the missing parameters of the model. A magnetic resonance image and many synthetic images were used for testing and evaluating the algorithm. The experiments show its robustness against noise.
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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
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