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Face recognitions in images
Krhut, Miloš ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
The master thesis deals with the topic of detecting faces in digital images. There are generally described and classified the most frequently used methods and discussed their advantages and disadvantages. More detailed is described skin color detection, eye and mouth detection and are teoretically described machine learning algorithms and detection based on Haar-classifiers. The work aims to implementation of these methods in the OpenCV library, it refers to practical application of them a finally compares different provided trained files.
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Monitoring of human body in videosequence
Plačko, Michal ; Šmirg, Ondřej (referee) ; Číka, Petr (advisor)
This thesis deals with human body detection and gestures tracking in videosequences. First, processing of videosequences in general is described. Further, different methods of human body detection are described and represented by significant papers. The most of the attention is focused on detection by real AdaBoost algorithm based on Haar-like features and Edgelet features. The practical part starts with selection of method that is implemented in this thesis. This method is detection by real AdaBoost based on Haar-like features. Further, different options of videosequence processing in JAVA are researched with justification of choice OpenCV library with JavaCV wrapper, which is used in this thesis. In the end, application itself is described, including description of GUI and description of each class and its functionality.
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Detection of racist symbols in pictures
Klapal, Matěj ; Říha, Kamil (referee) ; Povoda, Lukáš (advisor)
Goal of this thesis is detector of racist symbols from the picture using functions from the open source library OpenCV. Text also summarizes description of basic processes of image processing via computers. This text contains descriptions of some methods from the library allowing us to train and afterwards detect and localize requested object. This text also compares accuracy of detection using Haar-like features, Local Binary Patterns (LBP) and histogram of oriented gradients. Text also summarizes results of a test of detection for three supported symbols, swastika, signs of SS and triskelion.
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Computer Application Control by Natural Head Movement
Vojvoda, Jakub ; Materna, Zdeněk (referee) ; Beran, Vítězslav (advisor)
The aim of this work is to design and implement a system that tracks user's head in the input video frames and on the basis of its position achieves interaction with computer applications. Four methods for head detection have been proposed based on computer vision techniques as face detection by Haar-like features, background detection, Camshift or Lucas-Kanade to calculate an optical flow. The individual methods have been tested on recorded and in this field used data sets, and evaluated. The implementation of the system was then used to build the demo applications.
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AdaBoost in Computer Vision
Hradiš, Michal ; Zemčík, Pavel (referee) ; Potúček, Igor (advisor)
In this thesis, we present the local rank differences (LRD). These novel image features are invariant to lighting changes and are suitable for object detection in programmable hardware, such as FPGA. The performance of AdaBoost classifiers with the LRD was tested on a face detection dataset with results which are similar to the Haar-like features which are the state of the art in real-time object detection. These results together with the fact that the LRD are evaluated much faster in FPGA then the Haar-like features are very encouraging and suggest that the LRD may be a solution for future hardware object detectors. We also present a framework for experiments with boosting methods in computer vision. This framework is very flexible and, at the same time, offers high learning performance and a possibility for future parallelization. The framework is available as open source software and we hope that it will simplify work for other researchers.
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PC control via eyes
Neuwirth, Tomáš ; Číp, Pavel (referee) ; Horák, Karel (advisor)
The presented master thesis deals with the evaluation of the position of the iris compared with surroundings of the eye. This technique is supposed to be use for computer control. The created software works in real-time mode, the pictures are taken with an ordinary webcam. The first part of the work presents basic algorithms used in computer vision for edit of images. The following part is focused on abilities of methods to find the face, eyes and to detect the iris. The detection and the subsequent separation of the face is based on the recognition of skin colour in the YCbCr color space. The position of eye is then searched in the face by Haar-like features. The darkest part of the eye is found by horizontal projection from surroundings and the seed point is started from this place. From the area which is filled by the seed method (Flood Fill) and which shows the iris, the cursor movement is controlled by obtained x and y position.
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Face detection system
Karásek, Miroslav ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
This work deals with methods of computer vision for localization faces in an image segmentation of individual parts of face and its comparison with the face in the reference frame. The paper gives a brief overview of biometric methods useful to identify people. It describes the various methods of locating faces in images and their features. It also deals with the editing of digital images, with the design of my own methods of localization and verification of faces and the subsequent implementation of these methods.
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Application of AdaBoost
Wrhel, Vladimír ; Šilhavá, Jana (referee) ; Hradiš, Michal (advisor)
Basics of classification and pattern recognitions will be mentioned in this work. We will focus mainly on AdaBoost algorithm, which serves to create a strong classifier function by some weak classifiers. We shall get acquainted with some modifications of AdaBoost. These modifications improve some of AdaBoost attributes. We shall also look into weak classifiers and features applicable to them. We shall especially look into the Haar- likes features. We shall discus possibilities of using the mentioned algorithms and features in facial expression recognition. We shall describe the situation between facial expression databases. We shall draw out a possible implementation of application of facial expression recognition.
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Object Detection in Images
Ptáček, Tomáš ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
This work deals with the problem of object detection in images and describes theoretical backgrounds of detection based on boosting, AdaBoost algorithm and Haar-like features as weak classifiers. Further this work engages in design and implementation of a training and detection application based on OpenCV and wxWidgets libraries. To the end it shows a training and face detection test performed in the implemented application.
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