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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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System for Automatic Parking Access Based on License Plate Recognition
Václavek, Patrik ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
Goal of this thesis was to design and implement system operating in real time, which manages to detect incoming vehicle to the car park terminal, recognize its licence plate and automatically decide on its admission. System uses the Gaussian Mixture Model algorithm for detection of incoming vehicle. For reliable localization of licence plate are used two methods, the first one uses of extraction of Maximally Stable Extremal Regions (MSERs), the second one uses of Top-Hat transformation. Support Vector Machine (SVM) algorithm is used to decide, whether is the found area a licence plate. Character classification is performed using artificial neural network. For implementation was used library OpenCV. Thanks to optimalization is the extraction of MSERs accelerated up to seven times. The accomplished success rate in case of licence plate localization is 92,47% and in case of classification of characters is 90,03%.
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System for Automatic Parking Access Based on License Plate Recognition
Václavek, Patrik ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
Goal of this thesis was to design and implement system operating in real time, which manages to detect incoming vehicle to the car park terminal, recognize its licence plate and automatically decide on its admission. System uses the Gaussian Mixture Model algorithm for detection of incoming vehicle. For reliable localization of licence plate are used two methods, the first one uses of extraction of Maximally Stable Extremal Regions (MSERs), the second one uses of Top-Hat transformation. Support Vector Machine (SVM) algorithm is used to decide, whether is the found area a licence plate. Character classification is performed using artificial neural network. For implementation was used library OpenCV. Thanks to optimalization is the extraction of MSERs accelerated up to seven times. The accomplished success rate in case of licence plate localization is 92,47% and in case of classification of characters is 90,03%.
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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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