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Retinal biometry for human recognition
Sikorová, Eva ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This master thesis deals with recognition of a person by comparing symptom sets extracted from images of the retinal vessels pattern. The first part includes the insight into biometric issues, the punctual analysis of human identification using retina images, and especially the literature research of methods of extraction and comparison. In the practical part there were realized algorithms for human identification with the method of nearest neighbor search (NS), translation, template matching (TM) and extended NS and TM including more symptoms, for which MATLAB program was used. The thesis includes testing of suggested programs on the biometric database of symptomatic vectors with the following evaluation.
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Automatic Selection of Representative Pictures
Bartoš, Peter ; Svoboda, Pavel (referee) ; Polok, Lukáš (advisor)
There are billions of photos on the internet and as the size of these digital repositories grows, finding target picture becomes more and more difficult. To increase the informational quality of photo albums we propose a new method that selects representative pictures from a group of photographs using computer vision algorithms. The aim of this study is to analyze the issues about image features, image similarity, object clustering and examine the specific characteristics of photographs. Tests show that there is no universal image descriptor that can easily simulate the process of clustering performed by human vision. The thesis proposes a hybrid algorithm that combines the advantages of selected features together using a specialized multiple-step clustering algorithm. The key idea of the process is that the frequently photographed objects are more likely to be representative. Thus, with a random selection from the largest photo clusters certain representative photos are obtained. This selection is further enhanced on the basis of optimization, where photos with better photographic properties are being preferred.
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Gesture Recognition
Svoboda, Tomáš ; Mlích, Jozef (referee) ; Hradiš, Michal (advisor)
This Bachelor's thesis is engaged in recognition hand gestures. The advantages and disavantages of various color models for skin color detection are discussed here. Skin is detected by look-up table. Look-up table is created from histogram of skin color and optional from Gaussian distribution, whose parameters are estimated from histogram. Hidden Markov models are used for gesture classification. The HTK toolkit have been used for working with the models. Own decoder of Hidden Markov models based on Viterbi algorithm was created for real-time gesture recognition. Several experiments were accomplished with data sets for 4 gestures. The results of the experiments are very good.
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Detection and Recognition of Traffic Signs
Vránsky, Radovan ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This bachelor thesis is about different methods of detection and recognition of traffic signs in pictures. The introduction several of these methods are described and their use is demonstrated. In the next part of the thesis, the implementation of the detection and recognition of traffic signs with the use of Support Vector Machine is described in detail. It also describes the method of creating of the dataset or different models describing this dataset. In the conclusion the method is evaluated.
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Processing of User Reviews
Cihlářová, Dita ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.
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Processing of User Reviews
Cihlářová, Dita ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.
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Detection and Recognition of Traffic Signs
Vránsky, Radovan ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This bachelor thesis is about different methods of detection and recognition of traffic signs in pictures. The introduction several of these methods are described and their use is demonstrated. In the next part of the thesis, the implementation of the detection and recognition of traffic signs with the use of Support Vector Machine is described in detail. It also describes the method of creating of the dataset or different models describing this dataset. In the conclusion the method is evaluated.
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Gesture Recognition
Svoboda, Tomáš ; Mlích, Jozef (referee) ; Hradiš, Michal (advisor)
This Bachelor's thesis is engaged in recognition hand gestures. The advantages and disavantages of various color models for skin color detection are discussed here. Skin is detected by look-up table. Look-up table is created from histogram of skin color and optional from Gaussian distribution, whose parameters are estimated from histogram. Hidden Markov models are used for gesture classification. The HTK toolkit have been used for working with the models. Own decoder of Hidden Markov models based on Viterbi algorithm was created for real-time gesture recognition. Several experiments were accomplished with data sets for 4 gestures. The results of the experiments are very good.
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Automatic Selection of Representative Pictures
Bartoš, Peter ; Svoboda, Pavel (referee) ; Polok, Lukáš (advisor)
There are billions of photos on the internet and as the size of these digital repositories grows, finding target picture becomes more and more difficult. To increase the informational quality of photo albums we propose a new method that selects representative pictures from a group of photographs using computer vision algorithms. The aim of this study is to analyze the issues about image features, image similarity, object clustering and examine the specific characteristics of photographs. Tests show that there is no universal image descriptor that can easily simulate the process of clustering performed by human vision. The thesis proposes a hybrid algorithm that combines the advantages of selected features together using a specialized multiple-step clustering algorithm. The key idea of the process is that the frequently photographed objects are more likely to be representative. Thus, with a random selection from the largest photo clusters certain representative photos are obtained. This selection is further enhanced on the basis of optimization, where photos with better photographic properties are being preferred.
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Visualization of Cell Image Data
Černák, Michal ; Juránková, Markéta (referee) ; Juránek, Roman (advisor)
This thesis deals with extraction of data from cell images and their visualisation. Cell images are processed by FISH method. It discusses theory of diagnosis evaluation automation and cell features visualization. That concerns image processing, cell nuclei segmentation, feature extraction and data visualization.
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