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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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Face Features Extraction Methods
Adamček, Ľubomír ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The face has been one of the most attractive available human biometries for a long time due to it's easy and convenient way to obtain it. Possibilities of it's utilization are broad - from security, through monitoring up to enternainment industry. This work presents the domain of face biometry and analyses 3 extraction methods of facial features - PCA, LBP and HOG. A part of this work is also an efficient implementation of these algorithms including GUI designed to experiment with this implementation and to evaluate its performance on a set of images capturing people in various conditions.
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Automatic Photography Categorization
Matuszek, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deal with choosing methods, design and implementation of application, which is able of automatic categorization photos based on its content into predetermined groups. Main steps of categorization are described in greater detail. Finding and description of interesting points in image is implemented using SURF, creation of visual dictionary by k-means, mapping on the words through kd-tree structure. Own evaluation is made for categorization. It is described, how the selected steps were implemented with OpenCV and Qt libraries. And the results of runs of application with different settings are shown. And efforts to improve outcome, when the application can categorize right, but success is variable.
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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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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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Web defects classification
Janošík, Zdeněk ; Petyovský, Petr (referee) ; Honec, Peter (advisor)
In this master thesis is described how to design and implement classifier of defects detected during the final stage of production nonwovens. The beginning of the thesis is devoted to the analysis of options for image processing and classification. Followed by the part, where is described process of image segmentation and extraction of feature vector. Description of classifier implementation and table of achieved results of classification on real images of detected defects.
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Retinal Biometry for Human Recognition
Sikorová, E.
This paper deals with recognition of a person by comparing symptom sets extracted from images of the retinal vessels pattern. In the practical part there were realized algorithms for human identification with the method of nearest neighbor search (NS), extended NS including more symptoms and template matching, for which MATLAB program was used. The work includes testing of suggested programs on the biometric database of symptomatic vectors with the following evaluation.
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