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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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Musical genre classification
Káčerová, Erika ; Říha, Kamil (referee) ; Uher, Václav (advisor)
The aim of this bachelor thesis is creating a system for automatic music genre recognition. The thesis deals with two main issues, which are feature extraction of a genre and machine learning process. For the purpose of feature extraction a source code is written in JAVA programming language based on jAudio library. Six machine learning models are created in RapidMiner Studio software. The most appropriate one of them, Neural Networks method is then improved and tested on different parts of songs from database.These database contains 250 training songs and 25 test songs from five music genres: classical music, disco, drum and bass, hip hop and rock.
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Hard and soft exudates detection in retinal images
Válková, Hana ; Lamoš, Martin (referee) ; Kolář, Radim (advisor)
The thesis deals with automatic detection of soft and hard exudates in retinal images of the human eye. In its introduction the thesis describes the issue of diabetes in relation to the damage to the retina of the eye. What is described in the first place is diabetic retinopathy, its symptoms and progression of the disease. Another section is devoted to describing DIARETDB1, the freely accessible database which besides other things contains a set of images showing various degrees of disease, evaluation of images from the experts and the evaluation protocol. The next section discusses several methods for automatic detection of hard and soft exudates. The practical part of the bachelor’s thesis is aimed at image pre-processing with respect to the normalization of retinal images, the selected method for adaptive transformation of contrast was implemented. This part also containts description of chosen methology of thresholding, feature extraction based on lesions intensity and its surroundings, use of Ho Kashyap classifier is described, classification of lesions in images is followed. In conclusion realized methods is evaluated.
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Optical methods of gesture recognition
Netopil, Jan ; Odstrčilík, Jan (referee) ; Čmiel, Vratislav (advisor)
This thesis deals with optical devices and methods image processing for recognizing hand gestures. The types of gestures, possible applications, contact based devices and vision based devices are described in thesis. Next, a review of hand detection, features extraction and gesture classification is provided. Proposed gesture recognition system consists of infrared camera FLIR A655sc, infrared FLIR Lepton module, webcam Logitech S7500, method for hand gesture analysis and a database of gestures for classification. For each of the devices, gesture recognition is evaluated in terms of speed and accuracy in different environments. The proposed method was implemented in MATLAB.
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Object Classification Using Radar
Přívara, Jan ; Zemčík, Pavel (referee) ; Maršík, Lukáš (advisor)
The aim of this bachelor's thesis is to design and implement classification system using radar, specifically vehicle classification system. The first part describes both radar principles and radar signal processing methods. A brief introduction to machine learning is provided, with emphasis on Support Vector Machines classification model. Feature extraction methods from radar signal are discussed as well. The next part describes concept and implementation of system for vehicle classification. In the end, the implemented classification system is evaluated and the possible continuation of this work is stated.
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Simple Character Recognition
Hamrský, Jan ; Svoboda, Pavel (referee) ; Polok, Lukáš (advisor)
This work deals with the process of text location and recognition in an image document. It discusses the matter of feature extraction and its usage in machine learning. Portion of this work is devoted to design and implementation of application for simple character recognition of machine printed text.
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Voice Activity Detection
Břenek, Roman ; Grézl, František (referee) ; Matějka, Pavel (advisor)
This thesis describes techniques for voice activity detection in audio recordings. It is necessary to correctly classify all non-speech segments and recognize speech with noisy background. The whole process of voice activity detection (VAD) is described in this thesis, i.e. digitizing audio signal, feature extraction, training of the system, post-processing and final evaluation. There are three different systems compared within the thesis . The first one is based on phoneme recognition using neural network, the other two are variations of Gaussian Mixture Models (GMM). Each system was tested on three data sets - Tactical Speaker Identification Speech Corpus (TSID), Ham Radio (HR) and Rich Transcription Evaluation (RT05-RT07). The best results of each system are compared with the results of the third side.
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OCR on iOS Platform
Hakulin, Lukáš ; Žák, Pavel (referee) ; Angelov, Michael (advisor)
This thesis is dedicated to text recognition in image on iOS mobile platform. It describes principles and methods for text location, feature extraction and classification. Portion of this work is devoted to design and implementation of simple application. With this application is possible to recognize information about location of furniture in IKEA's storeroom.
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