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Content-Based Searching for Similar CT Scans
Hošek, Pavel ; Klíma, Ondřej (referee) ; Španěl, Michal (advisor)
The work deals with the design and implementation of tool for Content-Based Searching for similar CT scans. This work also deals with comparision of individual methods for searching for similar CT scans. Third-party libraries are used to extract individual descriptors. Furthermore, this work presents results of the implemented searching tool and also presents results of the comparision of individual methods for Content-Based Searching for similar CT scans and suggests possible improvements.
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Automatic Image Labelling
Lukáč, Michal ; Řezníček, Ivo (referee) ; Hradiš, Michal (advisor)
This thesis focuses on automatic image labelling to semantic categories. It describes the theory of classif cation and local features detection. It explains fundamental machine learning models used for image tagging, and how such models can be learned with Gradient descent. It propose solution with hierarchy for ImageNet and tagging images with attributes. MapReduce computing model is considered for learning on big data sets. In the last part it is described implementation, experimental and test results.
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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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Raster Image Processing Using FPGA
Musil, Petr ; Kadlček, Filip (referee) ; Zemčík, Pavel (advisor)
This thesis describes the design and implementation of hardware unit to detect objects in the image. Design of unit is optimized for fast streaming processing. Object detection is performed by the trained classifiers using local image features. It describes a new technique for multi-scale detection. Detector used accelerating algorithm based on neighboring positions. The correct functionality of the detector is verified by simulation and part of a whole is implemented on development kit.
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Image search using similarity measures
Harvánek, Martin ; Mašek, Jan (referee) ; Burget, Radim (advisor)
There are these methods implemented: circular sectors, color moments, color coherence vector and Gabor filters, they are based on low-level image features. These methods were evaluated after their optimal parameters were found. The finding of optimal parameters of methods is done by measuring of classification accuracy of learning operators and usage of operator cross validation on images in program RapidMiner. Implemented methods are evaluated on these image categories - ancient, beach, bus, dinousaur, elephant, flower, food, horse, mountain and natives, based on total average precision. The classification accuracy result is increased by 8 % by implemented modification (HSB color space + statistical function median) of original method circular sectors. The combination of methods color moments, circular sectors and Gabor filters with weighted ratio gives the best total average precision at 70,48 % and is the best method among all implemented methods.
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Image analysis in tribodiagnostics
Machalík, Stanislav ; Stodola,, Jiří (referee) ; Tillová,, Eva (referee) ; Zemčík, Pavel (advisor)
Image analysis of wear particles is a suitable support tool for detail analysis of engine, gear, hydraulic and industrial oils. It allows to obtain information not only of basic parameters of abrasion particles but also data that would be very difficult to obtain using classical ways of evaluation. Based on the analysis of morphological or image characteristics of particles, the progress of wearing the machine parts out can be followed and, as a result, possible breakdown of the engine can be prevented or the optimum period for changing the oil can be determined. The aim of this paper is to explore the possibilities of using the image analysis combined with the method of analytical ferrography and suggest a tool for automated particle classification. Current methods of wear particle analysis are derived from the evaluation that does not offer an exact idea of processes that take place between the friction surfaces in the engine system. The work is based upon the method of analytical ferrography which allows to evaluate the state of the machine. The benefit of use of classifiers defined in this wirk is the possibility of automated evaluation of analytical ferrography outputs; the use of them eliminates the crucial disadvantage of ferrographical analysis which is its dependence on the subjective evaluation done by the expert who performs the analysis. Classifiers are defined as a result of using the methods of machine learning. Based on an extensive database of particles that was created in the first part of the work, the classifiers were trained as a result, they make the evaluation of ferrographically separated abrasion particles from oils taken from lubricated systems possible. In the next stage, experiments were carried out and optimum classifier settings were determined based on the results of the experiments.
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Pattern Recognition in Image Using Classifiers
Juránek, Roman ; Španěl, Michal (referee) ; Herout, Adam (advisor)
An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the method of construction of strong classifiers will be explained. WaldBoost extension to the algorithm will be described. The thesis deals with image features that are often used as element of weak classifiers. Brief introduction to pattern recognition in context of computer vision will be outlined in the begining of the work. Also some widely used methods of classifier training will be presented. An object detection library based on AdaBoost classifiers was developed as part of the work. The library was used in implementation of software that in praktice demonstrates object detection in videosquences. Last part of the work describes tool for training of AdaBoost classifiers.
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Detection of Vehicles in Image
Pomykal, Antonín ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This work deals with the possibility of detection of cars in the image using the characteristics of cars with custom created image features , which are made pursuant to Haar-like features, and using methods of AdaBoost to train and their detection. We introduce the possibilities and types of custom picture features, OpenCV library, which was used in the implementation of the program, and we show the results and the success of this combination of detection algorithms.
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Automatic Photography Categorization
Gajová, Veronika ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
Purpose of this thesis is to design and implement a tool for automatic categorization of photos. The proposed tool is based on the Bag of Words classification method and it is realized as a plug-in for the XnView image viewer. The plug-in is able to classify a selected group of photos into predefined image categories. Subsequent notation of image categories is written directly into IPTC metadata of the picture as a keyword.
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Content-based Image Search
Talaš, Josef ; Surynek, Pavel (advisor) ; Děchtěrenko, Filip (referee)
This work aims at content-based image search. Different approaches to this type of search are investigated. The main focus of the thesis is special category of content-based image search called sketch-based image search. The most important descriptor types used for image feature extraction in image search are analyzed. Main contribution of the thesis to this research area is a new feature extraction method based on sketch-based image search. This method is implemented together with search interface. The method was evaluated by three test persons. The testing results show promising properties of new method and suggest further possible improve-ments. Powered by TCPDF (www.tcpdf.org)
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