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Automatic Content-Based Image Categorization
Němec, Ladislav ; Španěl, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with automatic content-based image classification. The main goal of this work is implementation of application which is able to perform this task automatically. The solution consists of variable system using local image features extraction and visual vocabulary built by k-means method. Bag Of Words representation is used as a global feature describing each image. Support Vector Machines - the final component of this system - perform the classification based on this representation. In the last chapter, the results of this experimental system are presented.
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Determination of Motion Parameters in Machine Vision
Dušek, Stanislav ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This thesis describe about determination of camera motion parameters in plane. At first there are introduce the basics of motion tracking, is focused to find out displacement between two input images. Below is describe the algorithm GoodFeatruresToTrack, which find out the most significant point in a first image. The point is search out the good point, which will be easy to track in next image, reduce the data volume and prepare the input information (array of significant point) for the algorithm Lucas-Kanade optical flow. In second part is deal with processing and utilization estimations optical flow. There is median filtration, below is describe computation of homogenous transformation, which describe all affine transformation in affine space. As the result are coordinates, which describe the shift between the two input images as X-axis and Y-axis value. The project used the library Open Computer Vision.
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Optic flow software module design
Grulich, Jan ; Růžička, Michal (referee) ; Věchet, Stanislav (advisor)
This bachelor thesis is focused on the analysis of optical flow. The first part is dedicated to the theoretical analysis of the motion of image features. Especially important is finding quality features that can be tracked in a videosequence. Detailed mathematical description of the necessary algorithms is presented. The second part describes the software and its functions with the implemented algorithms. The main function is determining the distance of an object from the camera based on its velocity. Empirical tests of this function are presented.
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