Browsing by Subject "Data compression (Computer science)"

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Open Access
Historical document analysis based on word matching
(2011) Arifoğlu, Damla
Historical documents constitute a heritage which should be preserved and providing automatic retrieval and indexing scheme for these archives would be beneficial for researchers from several disciplines and countries. Unfortunately, applying ordinary Optical Character Recognition (OCR) techniques on these documents is nearly impossible, since these documents are degraded and deformed. Recently, word matching methods are proposed to access these documents. In this thesis, two historical document analysis problems, word segmentation in historical documents and Islamic pattern matching in kufic images are tackled based on word matching. In the first task, a cross document word matching based approach is proposed to segment historical documents into words. A version of a document, in which word segmentation is easy, is used as a source data set and another version in a different writing style, which is more difficult to segment into words, is used as a target data set. The source data set is segmented into words by a simple method and extracted words are used as queries to be spotted in the target data set. Experiments on an Ottoman data set show that cross document word matching is a promising method to segment historical documents into words. In the second task, firstly lines are extracted and sub-patterns are automatically detected in the images. Then sub-patterns are matched based on a line representation in two ways: by their chain code representation and by their shape contexts. Promising results are obtained for finding the instances of a query pattern and for fully automatic detection of repeating patterns on a square kufic image collection.
Open Access
A novel compression algorithm based on sparse sampling of 3-D laser range scans
(2010) Dobrucalı, Oğuzcan
3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.