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A computer vision system for emotion recognition
Wójcik, Jan ; Bilík, Šimon (referee) ; Janáková, Ilona (advisor)
The term paper deals with the design of an emotion recognition system, which will be used as a communication tool for people with autism spectrum disorder. Camera data will be used for emotion recognition, so it will be a computer vision application. The work deals with areas such as face detection, extraction of relevant features, finding a suitable dataset or designing a classifier.
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Design of the application for the camera control and machine learning
Lukaszczyk, Jakub ; Richter, Miloslav (referee) ; Bilík, Šimon (advisor)
This bachelor thesis deals with the design of a program for controlling industrial cameras. The first part deals with current applications, their design and shortcomings. In the practical part, a similar application is then developed using Python. Compared to currently available applications, the developed application provides a modular and open design and can therefore be further extended and modified. The application is further complemented with a link to the Tensorflow library to enable image classification and training of artificial neural network models. The application has been tested and appears to be functional. The thesis concludes by evaluating the results and outlining possibilities for further development.
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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.
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Impact hammer calibration
Bilík, Šimon ; Klusáček, Stanislav (referee) ; Beneš, Petr (advisor)
The theoretical part of this thesis focus on the description of the piezoelectric accelerometers and their use for the impact measurements. It also characterizes the construction and the calibration process of the impact hammers with the use of the piezoelectric accelerometers. The practical part describes the place of calibration with the calibration tools, identifies the source of the oscillations on the output signal of the accelerometer and suggests its compensation. Part of the thesis is a service program for the impact measurement, analysis and the impact hammer calibration. Thesis also describes the methodology of the calibration and quantifies the measurement uncertainty.
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Driver monitoring
Pieger, Matúš ; Bilík, Šimon (referee) ; Richter, Miloslav (advisor)
This master’s thesis deals with the design of systems for data collection which describe the driver’s behaviour in a car. This data is used to detect risky behaviour that the driver may commit due to inattention caused by the use of either lower or higher levels of driving automation. The thesis first describes the existing safety systems, especially in relation to the driver. Then it deals with the design of the necessary measuring scenes and the implementation of new systems based on the processing of input images which are obtained via the Intel RealSense D415 stereo camera. Every system is tested in a real vehicle environment. In the end the thesis contains an evaluation regarding the detection reliability of the created algorithms, it considers their shortcomings and possible improvements.
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GUI for Automotive Tester
Bilík, Šimon ; Čala, Martin (referee) ; Beneš, Petr (advisor)
This thesis is aiming in problematic of shock absorber testing, namely by resonance adhesive test methodology EUSAMA. The goal of the thesis was to make a program for measuring and processing signals from shock absorber tester with a graphical user interface to operate of this program. The program was written in the LabVIEW programing language and for data acquisition was used acquisition hardware of the company National Instruments.
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A neural network for reconstruction of extinct animals
Pešek, David ; Bilík, Šimon (referee) ; Jirsík, Václav (advisor)
This work was focused on designing, learning and evaluating an artificial neural network for reconstructing extinct species. First, the main element of the proposed artificial neural network, i.e., the generative model, was selected. Given their excellent performance in the field of image generation, the class of diffusion models reasonably seemed to be the right choice. Specifically, the Stable diffusion model was chosen. One of the initial steps of the work was to create a training set for the proposed model. The animal images needed to be paired with some labels that could be used to identify the animal. For this purpose, the cytochrome c oxidase subunit I genes of the given animals were used. Furthermore, the sequential transformer model GPT-2, which is learned on the training set of human natural language, was used. This model was used to encode the DNA sequences into a vector form in which the semantics and context between the different parts of the DNA sequence were captured. The models would be very difficult to learn from scratch due to the large training set size required and the computational and time requirements. Thus, the GPT-2 model was only learned on the training set of DNA sequences of the passeriformes order, and the diffusion model itself was learned on pairs of images of these animals and DNA sequences encoded by the GPT-2 model. To generate the images, the original DNA sequences that resembled the sequences from the training set were generated using GPT-2. The encoding of these sequences was then passed to the diffusion model, which generated the images itself. The method of generating new DNA sequences using the GPT-2 model is based on the idea that the generated DNA sequence partially resembles the DNA sequences from the training set. Such experimentally generated DNA sequences may resemble DNA sequences of extinct ancestors or relatives of the passeriformes order. The model was in some cases able to generate images that could be considered as animal species , but it should be noted that often the generated images could not be considered as animal reconstructions. The success rate of generating a decent animal image was approximately 10%. The functionality of the model was also tested on a test set of DNA sequences of animals of several orders that fall under the class of birds as well as the order of passeriformes. The success rate of generating a reconstruction that could be compared to a photograph was around 5%.
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A computer vision system for emotion recognition
Wójcik, Jan ; Bilík, Šimon (referee) ; Janáková, Ilona (advisor)
The term paper deals with the design of an emotion recognition system, which will be used as a communication tool for people with autism spectrum disorder. Camera data will be used for emotion recognition, so it will be a computer vision application. The work deals with areas such as face detection, extraction of relevant features, finding a suitable dataset or designing a classifier.
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Railway wagons classification
Kotrlý, Michal ; Bilík, Šimon (referee) ; Honec, Peter (advisor)
This Master's thesis deals with classification of railway wagons based on visual information. A theoretical background of two different approaches for a classification system is provided and both approaches are subsequently implemented. First approach includes transforming images of wagons to histograms of visual words, according to the Bag of Visual Words method. Afterwards, classifiers such as k-NN, SVM, Multinomial Naive Bayes, neural network and Ensemble method, specifically Voting classifiers, are applied. Second approach is classifying images using well known architectures of Convolutional Neural Networks and transfer learning. AlexNet, VGG16 and ResNet50 were pre-trained on a large ImageNet dataset and the upper layers were trained on the dataset of railway wagons. Both approaches were fine-tuned for the best possible performance. For comparison of both approaches a training dataset with 1773 images in 27 classes and testing dataset with 444 images were compiled. On testing dataset the best classifier using BoVW method reached accuracy of 89%. Convolutional neural nets performed with 95-97% accuracy, which is an improvement. Prediction times of images to be classified are also considered. Beyond the scope of the assignment of this thesis, an algorithm for splitting train images into images of individual wagons was developed. In the conclusion, limitations and reasons for limited robustness of this algorithm are presented.
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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.
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