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Object detection
Baáš, Filip ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
This bachelor thesis deals with detection of rigid objects in images. Chamfer matching algorithm, which is built for this kind of tasks is used as detection algorithm. First part of this work is dedicated to theoretical explanation of the algorithm. Most commonly used metrics of distance transform are explained, which is needed for the algorithm. Also explanation of chamfer distance calculation and pyramid representation of information is here. Next part is dedicated to development tools used in this work, which is integrated development environment Visual Studio and libraries OpenCV for image processing and Qt for graphical user interface creation. In last part of this work, practical implementation of object detection is described. This part explains the way objects are rendered, steps for creating a template from rendered image, method to create set of templates, comparison of speed of distance transformation calculation in different metrics, comparison of speed of common and pyramid detection and method of score calculation. The conclusion summarizes reached goals of this work.
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Detection of object position
Baáš, Filip ; Janáková, Ilona (referee) ; Richter, Miloslav (advisor)
Master’s thesis deals with object pose estimation using monocular camera. As an object is considered every rigid, shape fixed entity with strong edges, ideally textureless. Object position in this work is represented by transformation matrix, which describes object translation and rotation towards world coordinate system. First chapter is dedicated to explanation of theory of geometric transformations and intrinsic and extrinsic parameters of camera. This chapter also describes detection algorithm Chamfer Matching, which is used in this work. Second chapter describes all development tools used in this work. Third, fourth and fifth chapter are dedicated to practical realization of this works goal and achieved results. Last chapter describes created application, that realizes known object pose estimation in scene.
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Detection of object position
Baáš, Filip ; Janáková, Ilona (referee) ; Richter, Miloslav (advisor)
Master’s thesis deals with object pose estimation using monocular camera. As an object is considered every rigid, shape fixed entity with strong edges, ideally textureless. Object position in this work is represented by transformation matrix, which describes object translation and rotation towards world coordinate system. First chapter is dedicated to explanation of theory of geometric transformations and intrinsic and extrinsic parameters of camera. This chapter also describes detection algorithm Chamfer Matching, which is used in this work. Second chapter describes all development tools used in this work. Third, fourth and fifth chapter are dedicated to practical realization of this works goal and achieved results. Last chapter describes created application, that realizes known object pose estimation in scene.
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Object detection
Baáš, Filip ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
This bachelor thesis deals with detection of rigid objects in images. Chamfer matching algorithm, which is built for this kind of tasks is used as detection algorithm. First part of this work is dedicated to theoretical explanation of the algorithm. Most commonly used metrics of distance transform are explained, which is needed for the algorithm. Also explanation of chamfer distance calculation and pyramid representation of information is here. Next part is dedicated to development tools used in this work, which is integrated development environment Visual Studio and libraries OpenCV for image processing and Qt for graphical user interface creation. In last part of this work, practical implementation of object detection is described. This part explains the way objects are rendered, steps for creating a template from rendered image, method to create set of templates, comparison of speed of distance transformation calculation in different metrics, comparison of speed of common and pyramid detection and method of score calculation. The conclusion summarizes reached goals of this work.
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