|
|
Visual Anomaly Detection in Industrial Production
Hrabica, Jan ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This thesis deals with the problem of unary classifiers for anomaly detection in industrial production. It starts with a discussion of classification as a general problem, classification methods and some of their evaluations, and then discusses the main categories of architectures used. Practical part describes the process of scene creation for the acquisitions of a datesed. Acquired dataset is then used for teaching a classifier, on which is then performer a number of experiments to determine its performance.
|
|
|
Unary Classification of Image Data
Beneš, Jiří ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.
|
|
|
Unary Classification of Image Data
Beneš, Jiří ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyperparameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of implementation of the unary classifier.
|
|
|
Autoencoder Implementation for Image Analysis
Sarančuk, Nikola ; Bilík, Šimon (referee) ; Horák, Karel (advisor)
The paper is devoted to the research of the problem of anomaly detection in industrial inspection. The paper describes the artificial neural network and its parts. The thesis contains a chapter where unary, binary and multi-class classifiers are compared. The thesis then explaines architecture of convolutional neural networks and autoencoder neural networks.. Then the paper describes the annotated dataset created. Finally, the paper describes the implementation of the convolutional autoencoder and evaluates the classification quality.
|
|
|
Autoencoder Implementation for Image Analysis
Sarančuk, Nikola ; Bilík, Šimon (referee) ; Horák, Karel (advisor)
The paper is devoted to the research of the problem of anomaly detection in industrial inspection. The paper describes the artificial neural network and its parts. The thesis contains a chapter where unary, binary and multi-class classifiers are compared. The thesis then explaines architecture of convolutional neural networks and autoencoder neural networks.. Then the paper describes the annotated dataset created. Finally, the paper describes the implementation of the convolutional autoencoder and evaluates the classification quality.
|
|
|
Unary Classification of Image Data
Beneš, Jiří ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyperparameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of implementation of the unary classifier.
|
|
|
Unary Classification of Image Data
Beneš, Jiří ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.
|
guest ::
