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Identification of persons via voice imprint
Mekyska, Jiří ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.
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Identification of persons using retinal biometry
Klimešová, Lenka ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
This paper deals with identification of persons using retinal biometry. The retinal vasculature is invariant and unique to everyone, which determines it for biometric purposes. The first part of the work includes information about biometrics, biometric systems and reliability measures. The next part describes the principle of using experimental video ophthalmoscope, which was used for retinal vascular imaging and includes the literature research of use of retinal images for biometrics, feature extraction methods and similarity measures. Finally, two algorithms to use the input data are proposed and realized in programming environment MATLAB®. The methods are tested and evaluated on a data set from experimental video ophthalmoscope and on publicly available STRaDe and DRIVE databases.
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Retinal biometry for human recognition
Sikorová, Eva ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This master thesis deals with recognition of a person by comparing symptom sets extracted from images of the retinal vessels pattern. The first part includes the insight into biometric issues, the punctual analysis of human identification using retina images, and especially the literature research of methods of extraction and comparison. In the practical part there were realized algorithms for human identification with the method of nearest neighbor search (NS), translation, template matching (TM) and extended NS and TM including more symptoms, for which MATLAB program was used. The thesis includes testing of suggested programs on the biometric database of symptomatic vectors with the following evaluation.
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Handwritten Character Recognition Using Artificial Neural Networks
Smejkal, Vojtěch ; Fapšo, Michal (referee) ; Plchot, Oldřich (advisor)
Thesis deals with handwritten block letters and digits recognition using artificial neural networks. Text segmentation algorithms, feature extraction methods and backpropagation learning are explained. There are also described performed experiments with variety of configurations on datasets. Application with graphical user interface and interactive mouse-written text recognition was created to train new neural networks and test their effectivity.
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Automatic image classification
Ševčík, Zdeněk ; Miklánek, Štěpán (referee) ; Sikora, Pavel (advisor)
The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.
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Pedestrians Tracking in a Video Record from a Stationary Camera
Trnkal, Milan ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This bachelor thesis focuses on pedestrians tracking from camera. In this work, I have introduced several methods of computer vision suitable for detection and classification of people. I proposed an algorithm for detecting and tracking pedestrians based on detection of movement. The application uses a Histogram of Oriented Gradients and SVM classifier together with color histograms for identification of pedestrians. Pedestrian's trajectories are then rendered to the output. Last part of the thesis deals with testing and evaluation of the results of the algorithm.
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Music Style Recognition
Behúň, Kamil ; Polok, Lukáš (referee) ; Hradiš, Michal (advisor)
This thesis deals with the music style recognition. The introduction is an overview of current methods used in the music style recognition. Next chapters deals with the system created for the music style recognition. The final system is consists of two feature extraction methods. The first uses the Mel-frequency cepstral coefficients extraction from records and the second uses feature extraction from spectrograms of records. The final system uses Support Vector Machine for classifying.
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Implementation of Simple Speech Recognizer in Android
Flajšingr, Petr ; Herout, Adam (referee) ; Szőke, Igor (advisor)
The subject of this thesis is an implementation and optimization of speech recognizer for operating system Android. This work covers implementation of recording of an audio signal and the subsequent feature extraction using Mel filter banks and neural network. It also contains information about implementation of dynamic decoder. The work focuses on implementation in low-level tools such as Android NDK and Renderscript and evaluates the success rate of the recognizer and its memory and time requirements.
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