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Boosting and Evolution
Mrnuštík, Michal ; Juránek, Roman (referee) ; Hradiš, Michal (advisor)
This thesis introduces combination of the AdaBoost and the evolutionary algorithm. The evolutionary algorithm is used to find linear combination of Haar features. This linear combination creates the feature to train weak classifier for AdaBoost. There are described basics of classification, Haar features and the AdaBoost. Next there are basic information about evolutionary algorithms. Theoretical description of combination of the AdaBoost and the evolutionary algorithm is included too. Some implementation details are added too. Implementation is tested on the images as part of the system for face recognition. Results are compared with Haar features.
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Face Detection
Štrba, Miroslav ; Juránek, Roman (referee) ; Hradiš, Michal (advisor)
This bachelor thesis contains overview of actual face detection methods using classifier. It also contains description of creating system for face detection. There are described different methods for classifier training in first part. There is analysis, which preceded creation of system focused on black-and-white picture, in second part. Implemented system is using WaldBoost algorithm and Haar features. There is option to use particle filter in video.
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Face Detection in Video
Kolman, Aleš ; Řezníček, Ivo (referee) ; Polok, Lukáš (advisor)
The project is focused on face detection in video. Firstly, it contains a summary of basic color models. Secondly, you can find the description and comparison of the basic methods for detection of human skin with a practical example of implementation of parametric detector. Thirdly, a theoretical basis for face detection and face tracking in a video containing a list of basic concepts and methods of this issue follows. Greater emphasis is placed on the description of machine learning algorithm AdaBoost and description of the possible application of the Kalman filter for the purpose of face tracking. Design, implementation and testing of library accomplished within the master thesis are listed in the final part of this thesis.
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Advanced Detection of Human Face
Koníček, Igor ; Juránek, Roman (referee) ; Herout, Adam (advisor)
With detection, localization and pose estimation of face was used to deal as three different tasks. This thesis works with algorithms which unify this tasks into one algorithm. To solve this problem histograms of oriented gradients connected in tree based structure are used. First part of work briefly informs about methods used for face detection. Aim of this work is to experiment with given algorithms and discover their quality for face detection and glasses detection.
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Face-Swap Camera for Android
Škorňok, Petr ; Páldy, Alexander (referee) ; Szentandrási, István (advisor)
The aim of this thesis is to explore existing possibilities of face detection on mobile devices supporting operating system Android and based on these findings create a face swap application with the input from the camera. The overall application is designed with an effort to reach the highest speed possible while processing the images. Implementation is tested from the view of the user and also from the point of program's speed and functionality.
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Automatic Face Recognition in Real Environment
Kičina, Pavol ; Šmirg, Ondřej (referee) ; Přinosil, Jiří (advisor)
This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.
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Image Processing Algorithms Optimization Using C++ Templates
Čepl, Radek ; Vyskočil, Michal (referee) ; Španěl, Michal (advisor)
Bachelor's thesis deals with image processing algorithm AdaBoost optimalization using C++ templates. Head aim of this thesis is effective evaluation of Haar Features with constant size. It also compares speed of feature detection on classical and template evaluation. The computer programme was written in C++ programming language using OpenCV graphic library and TinyXML library. Application was created and tested under Windows XP operating system.
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Face Anonymizer
Peša, Jan ; Juránek, Roman (referee) ; Láník, Aleš (advisor)
In this bachelor thesis you can find an overview of classification algorithms and their usage especially for searching image data and face detection. First part contains a brief introduction to a pattern recognition, a theoretical background of these algorithms and ways of training them. Other used components are also presented (e.g. Kalman filter or OpenCV library). Second part covers an implementation of the application which uses these technologies for searching, tracking and anononymization of human faces in a video stream.
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Object detection
Vítek, Pavel ; Kratochvíla, Lukáš (referee) ; Richter, Miloslav (advisor)
This paper deals with object detection where a human figure is used as a model. The introductory chapter defines terms detection and object. The second chapter describes the basic theory of computer vision , such as image processing and its parts. A substantial part is also focused on the Canny edge detection, upper body detection and pose detection. The third chapter describes the MATLAB integrated development environment in which the image processing functions are implemented. The last chapter is a practical demonstration of object detection. Different upper body detection methods are compared there. In the last subsection, two methods of determining the deviations based on the template are compared. Finally, all methods are compared with respect to the obtained results.
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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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