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Interactive 3D CT Data Segmentation Based on Deep Learning
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Kodym, Oldřich (advisor)
This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.
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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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Interactive 3D CT Data Segmentation Based on Deep Learning
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Kodym, Oldřich (advisor)
This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.
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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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