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Image Restoration Based on Convolutional Neural Networks
Svoboda, Pavel ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
A merit of this thesis is to introduce a unified image restoration approach based on a convolutional neural network which is to some degree degradation type independent. Convolutional neural network models were trained for two different tasks, a motion deblurring of license plate images and a removal of artifacts related to lossy image compression. The capabilities of such models are studied from two main perspectives. Firstly, how well the model can restore an image compared to the state-of-the-art methods. Secondly, what is the model's ability to handle several ranges of the same degradation type. An idea of the unified end-to-end approach is based on a recent development of neural networks and related deep learning in a field of computer vision. The existing hand-engineered methods of image restoration are often highly specialized for a given degradation type and in fact, define state of the art in several image restoration tasks. The end-to-end approach allows to directly train the required model on specifically corrupted images, and, further, to restore various levels of corruption with a single model. For motion deblurring, the end-to-end mapping model derived from models used in computer vision is deployed. Compression artifacts are restored with similar end-to-end based model further enhanced using specialized objective functions together with a network skip architecture. A direct comparison of the convolutional network based models and engineered methods shows that the data-driven approach provides beyond state-of-the-art results with a high ability to generalize over different levels of degradations. Based on the achieved results, this work presents the convolutional neural network based methods suggesting a possibility having the unified approach used for wide range of image restoration tasks.
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Detekce a lokalizace laserového paprsku ve vnějším prostředí
Horňanská, Lucie ; Baláž, Teodor (oponent) ; Drahanský, Martin (vedoucí práce)
Tato bakalářská práce se zabývá metodou detekce a lokalizace laserového paprsku ve vnějším prostředí. Teoretická část se zaměřuje na základní princip fungování laseru, jeho typy, legislativu, která jej upravuje, paprskovou optiku, typy kamer s možností použití fotografických filtrů, metody zpracování obrazu a detekce objektů v obraze. Praktická část představuje proces vytváření databáze snímků a popisuje návrh a implementaci algoritmu pro detekci laseru, jeho cílového bodu nebo zdroje v obrazu a následnou lokalizaci v trojrozměrném souřadném systému. Nakonec jsou vyhodnoceny dosažené výsledky vytvořeného programu a předloženy možnosti zlepšení.
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HUMAN ACTION RECOGNITION IN VIDEO
Řezníček, Ivo ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This thesis focuses on the improvement of human action recognition systems. It reviews the state-of-the-art in the field of action recognition from video. It describes techniques of digital image and video capture, and explains computer representations of image and video. This thesis further describes how local feature vectors and local space-time feature vectors are used, and how captured data is prepared for further analysis, such as classification methods. This is typically done with video segments of arbitrarily varying length. The key contribution of this work explores the hypothesis that the analysis of different types of actions requires different segment lenghts to achieve optimal quality of recognition. An algorithm to find these optimal lengths is proposed, implemented, and tested. Using this algorithm, the hypothesis was experimentally proven. It was also shown that by finding the optimal length, the prediction and classification power of current algorithms is improved upon. Supporting experiments, results, and proposed exploitations of these findings are presented.
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Detekce a lokalizace laserového paprsku ve vnějším prostředí
Horňanská, Lucie ; Baláž, Teodor (oponent) ; Drahanský, Martin (vedoucí práce)
Tato bakalářská práce se zabývá metodou detekce a lokalizace laserového paprsku ve vnějším prostředí. Teoretická část se zaměřuje na základní princip fungování laseru, jeho typy, legislativu, která jej upravuje, paprskovou optiku, typy kamer s možností použití fotografických filtrů, metody zpracování obrazu a detekce objektů v obraze. Praktická část představuje proces vytváření databáze snímků a popisuje návrh a implementaci algoritmu pro detekci laseru, jeho cílového bodu nebo zdroje v obrazu a následnou lokalizaci v trojrozměrném souřadném systému. Nakonec jsou vyhodnoceny dosažené výsledky vytvořeného programu a předloženy možnosti zlepšení.
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Image Restoration Based on Convolutional Neural Networks
Svoboda, Pavel ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
A merit of this thesis is to introduce a unified image restoration approach based on a convolutional neural network which is to some degree degradation type independent. Convolutional neural network models were trained for two different tasks, a motion deblurring of license plate images and a removal of artifacts related to lossy image compression. The capabilities of such models are studied from two main perspectives. Firstly, how well the model can restore an image compared to the state-of-the-art methods. Secondly, what is the model's ability to handle several ranges of the same degradation type. An idea of the unified end-to-end approach is based on a recent development of neural networks and related deep learning in a field of computer vision. The existing hand-engineered methods of image restoration are often highly specialized for a given degradation type and in fact, define state of the art in several image restoration tasks. The end-to-end approach allows to directly train the required model on specifically corrupted images, and, further, to restore various levels of corruption with a single model. For motion deblurring, the end-to-end mapping model derived from models used in computer vision is deployed. Compression artifacts are restored with similar end-to-end based model further enhanced using specialized objective functions together with a network skip architecture. A direct comparison of the convolutional network based models and engineered methods shows that the data-driven approach provides beyond state-of-the-art results with a high ability to generalize over different levels of degradations. Based on the achieved results, this work presents the convolutional neural network based methods suggesting a possibility having the unified approach used for wide range of image restoration tasks.
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HUMAN ACTION RECOGNITION IN VIDEO
Řezníček, Ivo ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This thesis focuses on the improvement of human action recognition systems. It reviews the state-of-the-art in the field of action recognition from video. It describes techniques of digital image and video capture, and explains computer representations of image and video. This thesis further describes how local feature vectors and local space-time feature vectors are used, and how captured data is prepared for further analysis, such as classification methods. This is typically done with video segments of arbitrarily varying length. The key contribution of this work explores the hypothesis that the analysis of different types of actions requires different segment lenghts to achieve optimal quality of recognition. An algorithm to find these optimal lengths is proposed, implemented, and tested. Using this algorithm, the hypothesis was experimentally proven. It was also shown that by finding the optimal length, the prediction and classification power of current algorithms is improved upon. Supporting experiments, results, and proposed exploitations of these findings are presented.
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