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Face Detection and Identification
Konôpková, Júlia ; Drahanský, Martin (referee) ; Váňa, Jan (advisor)
This work is focused on the problematic of face detection and identification in photography. The introduction is devoted to the most popular methods with briefly descriptions of their principles and rules. Within the practical part of this work we implement and test on free available databases the several of these methods. In the conclusion we evaluate the results and addition of this whole work.
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Classification of eMail Communication
Piják, Marek ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This diploma's thesis is based around creating a classifier, which will be able to recognize an email communication received by Topefekt.s.r.o on daily basis and assigning it into classification class. This project will implement some of the most commonly used classification methods including machine learning. Thesis will also include evaluation comparing all used methods.
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Firearm Type Identification in an Image
Čech, Ondřej ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
Main goal of this work is to design, implement and test an approach for classifying firearms in an image into categories with short and long fireams, and then with single shot, multi-barreled, repeating and semi-automatic/automatic firearms. This problem was solved using SVM classifier together with Harris corner detector, FREAK descriptor and Bag of Words method. Accuracy of final program is up to 13,3 %.
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Mapping of Road Signs Using Image Processing Methods
Leško, Vladimír ; Drahanský, Martin (referee) ; Novotný, Tomáš (advisor)
The following Bachelor's Thesis deals with traffic sign recognition in video clips, obtaining GPS records and inserting traffic sings with accurate GPS co-ordinates into OSM central database. In each of Thesis' chapters are in sequence introduced methods of image detection and classification, consecutive familiarization with OSM project and its JOSM plug-in. Afterwards there will be presented an application and an applicative plug-in design and implementation process. The final chapter is represented as the results of testing the application in 10 video clips. The conclusion contains an evaluation of the Thesis with possible extensions.
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Detection of groups of people in images
Mikulčík, Ondřej ; Zukal, Martin (referee) ; Číka, Petr (advisor)
This work describes two methods for detecting objects in images. The first method is the Viola-Jones, the second is the method of histograms oriented gradients. Start of work deals with the theoretical description of the methods. In the other parts of this work is presented creation of the training databases, implementation methods in the RapidMiner and their testing. In conclusion, the results and the use of methods for detection of groups of people in the database of images are evaluated.
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Controlling and Measuring Sport Drills by Voice/Sound
Odehnal, Jiří ; Křivka, Zbyněk (referee) ; Rychlý, Marek (advisor)
This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.
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Automated Web Page Categorization Tool
Lat, Radek ; Bartík, Vladimír (referee) ; Malčík, Dominik (advisor)
Tato diplomová práce popisuje návrh a implementaci nástroje pro automatickou kategorizaci webových stránek. Cílem nástroje je aby byl schopen se z ukázkových webových stránek naučit, jak každá kategorie vypadá. Poté by měl nástroj zvládnout přiřadit naučené kategorie k dříve nespatřeným webovým stránkám. Nástroj by měl podporovat více kategorií a jazyků. Pro vývoj nástroje byly použity pokročilé techniky strojového učení, detekce jazyků a dolování dat. Nástroj je založen na open source knihovnách a je napsán v jazyce Python 3.3.
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Detection and segmentation of lumbar vertebrae in 3D CT data
Nemček, Jakub ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the detection and the segmentation of lumbar vertebrae in CT image datas. The described detection method is based on the use of a trained SVM classificator and histograms of oriented gradients as the image features. The detection method is applied on two-dimensional sagital slices of the CT image. The segmentation method is implemented as triangular mesh model deformation of models, that are obtained from averaged vertebrae in real CT datas. The first part of the thesis describes essential theoretical knowledge about the anatomy of the axial skeleton, computer tomography, image processing methods and about the detection and segmentation issues. The second part contains the algorithms realisation description, the evaluation and the discussion of the results. Applications of the algorithms in CAD systems is described at the end. The application of all of the points is done in the programming software Matlab.
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Multi Object Class Learning and Detection in Image
Chrápek, David ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
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Detecting Objects in Images
Kubínek, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.
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