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Firearm Type Identification in an Image
Čech, Ondřej ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
Main goal of this work is to design, implement and test an approach for classifying firearms in an image into categories with short and long fireams, and then with single shot, multi-barreled, repeating and semi-automatic/automatic firearms. This problem was solved using SVM classifier together with Harris corner detector, FREAK descriptor and Bag of Words method. Accuracy of final program is up to 13,3 %.
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Embedded display recognition
Novotný, Václav ; Janáková, Ilona (referee) ; Honec, Peter (advisor)
This master thesis deals with usage of machine learning methods in computer vision for classification of unknown images. The first part contains research of available machine learning methods, their limitations and also their suitability for this task. The second part describes the processes of creating training and testing gallery. In the practical part, the solution for the problem is proposed and later realised and implemented. Proper testing and evaluation of resulting system is conducted.
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Region Detectors and Descriptors in Image
Žilka, Filip ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.
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Firearm Type Identification in an Image
Čech, Ondřej ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
Main goal of this work is to design, implement and test an approach for classifying firearms in an image into categories with short and long fireams, and then with single shot, multi-barreled, repeating and semi-automatic/automatic firearms. This problem was solved using SVM classifier together with Harris corner detector, FREAK descriptor and Bag of Words method. Accuracy of final program is up to 13,3 %.
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|
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Embedded display recognition
Novotný, Václav ; Janáková, Ilona (referee) ; Honec, Peter (advisor)
This master thesis deals with usage of machine learning methods in computer vision for classification of unknown images. The first part contains research of available machine learning methods, their limitations and also their suitability for this task. The second part describes the processes of creating training and testing gallery. In the practical part, the solution for the problem is proposed and later realised and implemented. Proper testing and evaluation of resulting system is conducted.
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|
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Region Detectors and Descriptors in Image
Žilka, Filip ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.
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