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Automatic image annotation
Hegmon, Jiří ; Karásek, Jan (referee) ; Burget, Radim (advisor)
Recognition and comparison of image is one of the main problems and area of the field of computer vision. This thesis adds to these two issues the third, the recognition image semantics, so called annotations or labels. This work uses the knowledge of methods of recognizing the similarity of images to create a tool that is able based on training dataset of images and annotations, create a group most likely annotation for the test set of images. This work presents several types of test datasets suitable for the detection of annotation information for images. Subsequently, best set with the necessary training dataset size and enough information about annotations is selected. Based on this training dataset algorithm is designed for easy loading test set without large demands on computer performance. Evaluation of annotation information is done based on different similarity algorithms. At the beginning of this work was to use a simple, but not very effective method of MSE and comparison of color histograms, but gradually it was necessary to move to using more advanced methods (such as Tamura, Gabor, CEDD nebo různé druhy hostistogramů). The results of this comparison are then taken to evaluate the likelihood of the annotation for the image specified test set. The last part is an evaluation of the accuracy of annotation based on information from the test set.
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Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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Comparison of Properties and Performance of Object Oriented Databases
Kozák, Daniel ; Burget, Radek (referee) ; Zelený, Jan (advisor)
In this thesis, the reader learns basic models, which are use for storing data in database sys- tems. Next I describe a way, how to store objects in Java Language and explain expressions like object-relational mapping, reflection and introspection. After that, I will introdice some of existing implementation, which are use for storing objects in Java. Next I will explain testing methodology and make benchmarks of various implementation. In the end I will analyze a results of these benchmarks.
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Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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Comparison of Properties and Performance of Object Oriented Databases
Kozák, Daniel ; Burget, Radek (referee) ; Zelený, Jan (advisor)
In this thesis, the reader learns basic models, which are use for storing data in database sys- tems. Next I describe a way, how to store objects in Java Language and explain expressions like object-relational mapping, reflection and introspection. After that, I will introdice some of existing implementation, which are use for storing objects in Java. Next I will explain testing methodology and make benchmarks of various implementation. In the end I will analyze a results of these benchmarks.
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Automatic image annotation
Hegmon, Jiří ; Karásek, Jan (referee) ; Burget, Radim (advisor)
Recognition and comparison of image is one of the main problems and area of the field of computer vision. This thesis adds to these two issues the third, the recognition image semantics, so called annotations or labels. This work uses the knowledge of methods of recognizing the similarity of images to create a tool that is able based on training dataset of images and annotations, create a group most likely annotation for the test set of images. This work presents several types of test datasets suitable for the detection of annotation information for images. Subsequently, best set with the necessary training dataset size and enough information about annotations is selected. Based on this training dataset algorithm is designed for easy loading test set without large demands on computer performance. Evaluation of annotation information is done based on different similarity algorithms. At the beginning of this work was to use a simple, but not very effective method of MSE and comparison of color histograms, but gradually it was necessary to move to using more advanced methods (such as Tamura, Gabor, CEDD nebo různé druhy hostistogramů). The results of this comparison are then taken to evaluate the likelihood of the annotation for the image specified test set. The last part is an evaluation of the accuracy of annotation based on information from the test set.
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