Abstract

Extracting 3D information from the set of digital images, using stereo algorithms, became common in several fields of computer vision. In this work, new method is presented, which can improve the performance of all area-based stereo algorithms. In some cases, the processing speed can be increased up to six times. This new method is called false epipolar constrain, as it is roughly based on the most common epiplar constrain.
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Abstract

Vectorization is usually the first step in pattern recognition of digital images. Several methods of image vectorization have been proposed so far, but most of them are highly specialized for specific set of images. In this work, a new method of image vectorization based on Divide & Conquer paradigm is presented. This method excels in speed and reliability, but the most important feature is its ability to solve most, even highly specific tasks. The method is so robust, that it can be applied even on real images (digital photos), where most other methods fail.
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Abstract

My work in computer graphics raised a requirement for complex tool for modeling of 3D objects. Instead of using some of already available software, a decision have been made to creating my own application. The Modeler is a advanced tool, able to create even complex objects like polyhedrons and subdivision surfaces and incorporate them into 3D model. Models can be subsequently animated, using joints and moving parts. Animations for a single model are a different objects, and several animations can be used for the same model.
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