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Detection of Graffiti Tags in Image
Pavlica, Jan ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti tags in the image. Graffiti tagsare the most common expression of graffiti, which serves as the author’s signature. In the thesis, state-of-the-art detection systems were tested; the most effective one is the Single Shot MultiBox Detector. The result has reached 75.7% AP.
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Board Game User Interface with a Camera
Cihlářová, Dita ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
Cílem této práce je vytvořit systém, který bude schopen nahradit reálného protihráče ve hře Dostihy a sázky. Hra je snímána kamerou, ze které se obraz přenáší do počítače, kde probíhá zpracování obrazu a identifikace herních objektů. Dále aplikace analyzuje stav hry a rozhodne o svém následujícím tahu. Rozhodovací algoritmus je schopen reagovat na každou herní situaci a tudíž dohrát hru do konce. Aplikaci mohou využít hráči, jež rádi hrají Dostihy a sázky, ale chybí jim protihráč. Aplikace také může najít uplatnění jako demostrace možností počítačového vidění.
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Application of computational methods in classification of glass stones
Lébl, Matěj ; Hnětynková, Iveta (advisor) ; Kopal, Jiří (referee)
Application of computational methods in classification of glass stones Bc. Matěj Lébl Abstrakt: The goal of this thesis is to employ mathematical image processing methods in automatic quality control of glass jewellery stones. The main math- ematical subject is a matrix of specific attributes representing digital image of the studied products. First, the thesis summarizes mathematical definition of digital image and some standard image processing methods. Then, a complete solution to the considered problem is presented. The solution consists of stone localization within the image followed by analysis of the localized area. Two lo- calization approaches are presented. The first is based on the matrix convolution and optimized through the Fourier transform. The second uses mathematical methods of thresholding and median filtering, and data projection into one di- mension. The localized area is analyzed based on statistical distribution of the stone brightness. All methods are implemented in the MATLAB environment. 1
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Online interest point detector
Přibyl, Jakub ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
This thesis focuses on online learning detector for long-term tracking of object in video sequence. The object is defined by a bounding box. The text describes different parts of the detector: object tracking, object detection and online learning detector. The main contribution of this work is creating extension of the OpenTLD program for parallel detection and tracking of multiple objects. The parallelization is then compared on two practical examples and the processor's impact on detection is compared. The best results were achieved with parallelization, where all objects were detected. The most accurate detection was in the case of sufficiently learned objects with the smallest shape change.
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Robotics Gun Turret for Perimeter Guarding - Target Detection
Kaplenko, Mykola ; Kopečný, Lukáš (referee) ; Žalud, Luděk (advisor)
The basis for the efficient operation of any autonomous device are control algorithms. Therefore, the bachelor thesis is dedicated to the management problems one of the most promising types of weapon - automatic weapon towers. In the framework of the given work there is an overview of the object detection methods in the picture and comparing the advantages and disadvantages of using different methods in different surrounding conditions. On the basis of the analysis, the light mark detecting algorithm (red dot from the laser pointer) is designed. The stepper motor control algorithm of the automatic weapon tower is designed on the basis on the performed theoretical calculations. For the declared algorithms are offering possible improvements and analyzing their strengths and weaknesses.
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License Plate Detection and Recognition
Řepka, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This paper addresses the problem of object detection and recognition from still images using methods of computer vision. The objects of detection are czech license plates and the goal of this paper was to create an automatic license plate anotation tool. Suggested solution uses edge detection and machine learned cascading classifiers. Created application was then tested on dataset taken by the author.
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Application for Recognition of Hematoma on Skin
Mihalčin, Tomáš ; Košík, Michal (referee) ; Drahanský, Martin (advisor)
This bachelor thesis contains a theoretical basic that introduces problem of skin hematoma formation, image segmentation using the OpenCV library and platform operating system Android. This thesis describes the design and implementation of application for recognition of hematoma on the skin. The application is developed for tablets running operation system Android and it will be used for medical purposes and to facilitate the work of medical.
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Traffic Monitoring from Aerial Video Data
Babinec, Adam ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis proposes a system for extraction of vehicle trajectories from aerial video data for traffic analysis. The system is designed to analyse video sequence of a single traffic scene captured by an action camera mounted on an arbitrary UAV flying at the altitudes of approximately 150 m. Each video frame is geo-registered using visual correspondence of extracted ORB features. For the detection of vehicles, MB-LBP classifier cascade is deployed, with additional step of pre-filtering of detection candidates based on movement and scene context. Multi-object tracking is achieved by Bayesian bootstrap filter with an aid of the detection algorithm. The performance of the system was evaluated on three extensively annotated datasets. The results show that on the average, 92% of all extracted trajectories are corresponding to the reality. The system is already being used in the research to aid the process of design and analysis of road infrastructures.
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Acceleration of Object Detection Using Classifiers
Juránek, Roman ; Kälviäinen, Heikki (referee) ; Sojka, Eduard (referee) ; Zemčík, Pavel (advisor)
Detekce objektů v počítačovém vidění je složítá úloha. Velmi populární a rozšířená metoda pro detekci je využití statistických klasifikátorů a skenovacích oken. Pro učení kalsifikátorů se často používá algoritmus AdaBoost (nebo jeho modifikace), protože dosahuje vysoké úspěšnosti detekce, nízkého počtu chybných detekcí a je vhodný pro detekci v reálném čase. Implementaci detekce objektů je možné provést různými způsoby a lze využít vlastnosti konkrétní architektury, pro urychlení detekce. Pro akceleraci je možné využít grafické procesory, vícejádrové architektury, SIMD instrukce, nebo programovatelný hardware. Tato práce představuje metodu optimalizace, která vylepšuje výkon detekce objektů s ohledem na cenovou funkci zadanou uživatelem. Metoda rozděluje předem natrénovaný klasifikátor do několika různých implementací, tak aby celková cena klasifikace byla minimalizována. Metoda je verifikována na základním experimentu, kdy je klasifikátor rozdělen do předzpracovací jednotku v FPGA a do jednotky ve standardním PC.
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