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1	Komparace pedagogických modelů EUR a 5E / Comparison of EUR and 5E, the instructional models Oktábcová, Jitka January 2019 (has links) This diploma thesis deals with the comparison of two schemes for students learning. Those are a three-phase E-R-R model, the parts of which are called Evocation, Realization of Meaning, Reflection. The second model is five-phase learning model 5Es, with the following parts: Engage, Explore, Explain, Elaborate, Evaluate. Comparison of the models was done by the method of action research at the Magic Hill Elementary School in Říčany during a complex student practicum in 2017. The research lasted for two weeks and involved two fourth grade classes. In total, 31 pupils participated in the research. The research was conducted alternately in both classes. On one topic, two lessons were always prepared, each of which was based on one of the comparative pedagogical models. Each day, one subject was taught in both classes, with class 4.A being done with E-U-R and class 4.B with 5E. Lectured subjects were Czech language and mathematics. The research has shown that the compared pedagogical models are very similar, although they differ in their structure and particularly in the number of phases. The E-R-R model is better suited to shorter learning units, while model 5E requires longer units for its efficiency. Both models can be modified according to the needs of the teacher, pupil, subject, etc., whereas...
2	Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces Taati, BABAK 01 September 2009 (has links) We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084 computer vision range data object recognition tracking local shape descriptor point matching pose estimation pose acquisition 3-D 3D point cloud satellite tracking optimization range image processing range image RANSAC registration alignment surface computational geometry detection localization model-based object identification point correspondence feature selection VD-LSD LSD genetic algorithm simulated annealing forward feature selection multivariate features subset selection local properties LIDAR dense stereo stereo precision feature matching machine learning training learning phase preprocessing

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Komparace pedagogických modelů EUR a 5E / Comparison of EUR and 5E, the instructional models

Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces