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Object Detection and Recognition in Image
Muzikářová, Michaela ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
This bachelor's thesis deals with design and implementation of client-server application for object recognition with the use of existing mobile application. Theoretical part describes the differences between human and computer vision, followed by information about object detection and recognition with selected methods. The next section provides a detailed overview of artificial neural networks, which were used for this work, with their qualities for object recognition. Following part examines selected mobile applications for object recognition, followed by existing frameworks and libraries with focus on artificial neural networks. Among these, Caffe Framework was selected for the work. The next section illustrates the progress of design and implementation and describes the system, along with experiments and dataset used to prove its functionality.
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Deep Learning for Medical Image Analysis
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This bachelor thesis deals with medical volume data analysis using convolutional neural networks. The input of the analysis is a CT scan of human limbs and the output are segmented countours of long bones, humerus and tibia. The goal of this work is to find suitable convolutional neural network settings to achieve the best possible analysis output while the area under the Precision-Recall curve is used as the precision metric. The best accuracy reaches almost 88 % (0.8778 AUC). The implementation is based on Caffe framework, or python caffe module.
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Convolutional Neural Networks for Emotion Recognition
Jileček, Jan ; Najman, Pavel (referee) ; Hradiš, Michal (advisor)
Convolutional neural networks are used for various tasks, but foremost in machine learning, in which they excel. This work is going to introduce some existing frameworks, other algorithms for recognition and then we describe the training dataset creation and the model for emotion recognition training process. Mentioned model has accuracy of 60%. It is used for emotion statistics retrieval from movie trailers. Model for genre recognition is created from those statistics and then finally used in our application for genre recognition of the input trailer, with best accuracy of 47%.
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Deep Learning for Medical Image Analysis
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This bachelor thesis deals with medical volume data analysis using convolutional neural networks. The input of the analysis is a CT scan of human limbs and the output are segmented countours of long bones, humerus and tibia. The goal of this work is to find suitable convolutional neural network settings to achieve the best possible analysis output while the area under the Precision-Recall curve is used as the precision metric. The best accuracy reaches almost 88 % (0.8778 AUC). The implementation is based on Caffe framework, or python caffe module.
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Convolutional Neural Networks for Emotion Recognition
Jileček, Jan ; Najman, Pavel (referee) ; Hradiš, Michal (advisor)
Convolutional neural networks are used for various tasks, but foremost in machine learning, in which they excel. This work is going to introduce some existing frameworks, other algorithms for recognition and then we describe the training dataset creation and the model for emotion recognition training process. Mentioned model has accuracy of 60%. It is used for emotion statistics retrieval from movie trailers. Model for genre recognition is created from those statistics and then finally used in our application for genre recognition of the input trailer, with best accuracy of 47%.
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Object Detection and Recognition in Image
Muzikářová, Michaela ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
This bachelor's thesis deals with design and implementation of client-server application for object recognition with the use of existing mobile application. Theoretical part describes the differences between human and computer vision, followed by information about object detection and recognition with selected methods. The next section provides a detailed overview of artificial neural networks, which were used for this work, with their qualities for object recognition. Following part examines selected mobile applications for object recognition, followed by existing frameworks and libraries with focus on artificial neural networks. Among these, Caffe Framework was selected for the work. The next section illustrates the progress of design and implementation and describes the system, along with experiments and dataset used to prove its functionality.
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