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New methods for super-resolution imaging
Kučera, Ondřej ; Rajmic, Pavel (referee) ; Špiřík, Jan (advisor)
This master's thesis deals with methods of increasing the image resolution. It contens as a description of theoretical principles and description of calculations which are wellknown nowdays and are usually used for increasing image resolution both description of new methods which are used in this area of image procesing. It also contens a method which I suggested myself. There is also a description of methods for an evaluation of image similarity and a comparation of results from methods which are described in this thesis. This thesis includes implementations of selected methods in programming language MATLAB. It was created an application, which realizes some methods of increasing image and evaluate their results relation to the original image using PSNR and SSIM index.
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A Classification Methods for Retinal Nerve Fibre Layer Analysis
Zapletal, Petr ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This thesis is deal with classification for retinal nerve fibre layer. Texture features from six texture analysis methods are used for classification. All methods calculate feature vector from inputs images. This feature vector is characterized for every cluster (class). Classification is realized by three supervised learning algorithms and one unsupervised learning algorithm. The first testing algorithm is called Ho-Kashyap. The next is Bayess classifier NDDF (Normal Density Discriminant Function). The third is the Nearest Neighbor algorithm k-NN and the last tested classifier is algorithm K-means, which belongs to clustering. For better compactness of this thesis, three methods for selection of training patterns in supervised learning algorithms are implemented. The methods are based on Repeated Random Subsampling Cross Validation, K-Fold Cross Validation and Leave One Out Cross Validation algorithms. All algorithms are quantitatively compared in the sense of classication error evaluation.
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Super-resolution methods
Franěk, Pavel ; Fedra, Petr (referee) ; Mézl, Martin (advisor)
The main goal of this bachelor’s thesis is acquaint with method, which enable increasing resolution digital photos. Also realize individual interpolation method and Super-resolution by the help of programme Matlab and reference on estimation record. Discuss possibility using method super- resolution for imagery with medical modality.
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Modern methods for protein secondary structure prediction and their comparison
Kraus, Ondřej ; Novotný, Marian (advisor) ; Pleskot, Roman (referee)
Today, there are several protein secondary structure predictors; most of them use algorithms such as hidden Markov models or artificial neural networks. Therefore I will introduce them to a reader in my thesis. I will explain their principles, as well as their advantages and disadvantages. The majority of contemporary predictors have accuracy 70%-80% for prediction of three types of protein secondary structure. However these results are only approximate, due to different testing methodology. Therefore the user should get familiar with the method and its testing methodology in detail at first. Key-words: protein structure prediction, hidden Markov model, artificial neural network, nearest neighbour, protein secondary structure
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Modern methods for protein secondary structure prediction and their comparison
Kraus, Ondřej ; Novotný, Marian (advisor) ; Pleskot, Roman (referee)
Today, there are several protein secondary structure predictors; most of them use algorithms such as hidden Markov models or artificial neural networks. Therefore I will introduce them to a reader in my thesis. I will explain their principles, as well as their advantages and disadvantages. The majority of contemporary predictors have accuracy 70%-80% for prediction of three types of protein secondary structure. However these results are only approximate, due to different testing methodology. Therefore the user should get familiar with the method and its testing methodology in detail at first. Key-words: protein structure prediction, hidden Markov model, artificial neural network, nearest neighbour, protein secondary structure
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Super-resolution methods
Franěk, Pavel ; Fedra, Petr (referee) ; Mézl, Martin (advisor)
The main goal of this bachelor’s thesis is acquaint with method, which enable increasing resolution digital photos. Also realize individual interpolation method and Super-resolution by the help of programme Matlab and reference on estimation record. Discuss possibility using method super- resolution for imagery with medical modality.
|
|
|
New methods for super-resolution imaging
Kučera, Ondřej ; Rajmic, Pavel (referee) ; Špiřík, Jan (advisor)
This master's thesis deals with methods of increasing the image resolution. It contens as a description of theoretical principles and description of calculations which are wellknown nowdays and are usually used for increasing image resolution both description of new methods which are used in this area of image procesing. It also contens a method which I suggested myself. There is also a description of methods for an evaluation of image similarity and a comparation of results from methods which are described in this thesis. This thesis includes implementations of selected methods in programming language MATLAB. It was created an application, which realizes some methods of increasing image and evaluate their results relation to the original image using PSNR and SSIM index.
|
|
|
A Classification Methods for Retinal Nerve Fibre Layer Analysis
Zapletal, Petr ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This thesis is deal with classification for retinal nerve fibre layer. Texture features from six texture analysis methods are used for classification. All methods calculate feature vector from inputs images. This feature vector is characterized for every cluster (class). Classification is realized by three supervised learning algorithms and one unsupervised learning algorithm. The first testing algorithm is called Ho-Kashyap. The next is Bayess classifier NDDF (Normal Density Discriminant Function). The third is the Nearest Neighbor algorithm k-NN and the last tested classifier is algorithm K-means, which belongs to clustering. For better compactness of this thesis, three methods for selection of training patterns in supervised learning algorithms are implemented. The methods are based on Repeated Random Subsampling Cross Validation, K-Fold Cross Validation and Leave One Out Cross Validation algorithms. All algorithms are quantitatively compared in the sense of classication error evaluation.
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