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Computer Interface Based on User's Head Position
Chmiel, Filip ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
This paper presents the method for a head pose and orientation detection based on computer vision techniques. The method is based on the AdaBoost approach for initial face recognition and the optical flow technique for further object tracking. The detection results are further interpreted and stabilized. The method is designed for usage in designing and building the innovative user interfaces.
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Recognizing Faces within Image
Svoboda, Pavel ; Žák, Pavel (referee) ; Švub, Miroslav (advisor)
The essence of face recognition within the image is generally computer vision, which provides methods and algorithms for the implementation. Some of them are described just in this work. Whole process is split in to three main phases. These are detection, aligning of detected faces and finally its recognition. Algorithms which are used to applied in given issue and which are still in progress from todays view are mentioned in every phase. Implementation is build up on three main algorithms, AdaBoost to obtain the classifier for detection, method of aligning face by principal features and method of Eigenfaces for recognizing. There are theoretically described except already mentioned algorithms neural networks for detection, ASM - Active Shape Models algorithm for aligning and AAM - Active Appearance Model for recognition. In the end there are tables of data retrieved by implemented system, which evaluated the main implementation.
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Detection of face parts in the thermographic spectrum
Šujan, Miroslav ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Master´s thesis deals with current problems of face detection and its parts in the infrared thermographic spectrum. Most previously published literature deals with the detection in the visible spectrum, making the thermographic detection range an interesting alternative. The work deals with the processing of image signals, images and faces in thermographic spectrum, selected methods of face detection and its parts and also deals with practical system design for detecting facial parts in this spectrum and its subsequent testing.
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Face Detection
Šašinka, Ondřej ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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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, Invariant to Rotation
Bureš, Václav ; Herout, Adam (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis focuses on the detection of type uniform objects (concretely faces) in an image. Furthermore the thesis concentrates on the detection of objects in various rotations. The thesis covers a brief overview of methods available, such as Logical Binary Patterns, Histogram Of Gradients, Eigen Faces and more closely specified AdaBoost. Next, freely available datasets are presented, with a descripiton of their chosen characteristics. At the end of the thesis, experiments using AdaBoost algorythm and their evaluation are described.
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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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Biometric Image Data Classifier
Tretter, Zdeněk ; Drahanský, Martin (referee) ; Doležel, Michal (advisor)
The aim of this thesis is to design and implement fingerprint classifier, which classifies the fingerprints based on the scanner used. Reader is presented with existing types of fingerprint scanners and phases of classification. Designed classifier is using a cascade of classifiers, trained using the AdaBoost learning algorithm. The application was implemented in the C++ language using OpenCV library for operational systems GNU/Linux and MS Windows.
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Recognition of Handwritten Digits
Štrba, Miroslav ; Španěl, Michal (referee) ; Herout, Adam (advisor)
Recognition of handwritten digits is a problem, which could serve as model task for multiclass recognition of image patterns. This thesis studies different kinds of algoritms (Self-Organizing Maps, Randomized tree and AdaBoost) and methods for increasing accuracy using fusion (majority voting, averaging log likelihood ratio, linear logistic regression). Fusion methods were used for combine classifiers with indentical train parameters, with different training methods and with multiscale input.
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Perimeter Monitoring and Intrusion Detection Based on Camera Surveillance
Goldmann, Tomáš ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This bachelor thesis contains a description of the basic system for perimeter monitoring. The main part of the thesis introduces the methods of computer vision suitable for detection and classification of objects. Furthermore, I devised an algorithm based on background subtraction which uses a Histogram of Oriented Gradients for description of objects and an SVM classifier for their classification. The final part of the thesis consists of a comparison of the descriptor based on the Histogram of Oriented Gradients and the SIFT descriptor and an evaluation of precision of the detection algorithm.
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