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Line tracking for delivery robot
Juhas, Miroslav ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This thesis describes basics of methods and algorithms used in computer vision and application of them at a simple practical problem – line-tracking for delivery robot. The first part of this thesis contains basic theoretical knowledge of computer vision, which is important for understanding the problem. It is an introduction to problems of computer vision. The second part of thesis describes solving of particular steps, which are image preprocessing, segmentation, trajectory detection and algorithms for direction control. It contains outcomes of particular steps and selection of methods acceptable for solving the problem. There are presented experiences with tests of algorithms on the UTAR platform in context of this work. The last part of thesis is evaluating results taken during work.
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Web defects classification
Janošík, Zdeněk ; Petyovský, Petr (referee) ; Honec, Peter (advisor)
In this master thesis is described how to design and implement classifier of defects detected during the final stage of production nonwovens. The beginning of the thesis is devoted to the analysis of options for image processing and classification. Followed by the part, where is described process of image segmentation and extraction of feature vector. Description of classifier implementation and table of achieved results of classification on real images of detected defects.
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Traffic signs detection and recognition
Dvořák, Michal ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
The goal of this thesis is the utilization of computer vision methods, in a way that will lead to detection and identification of traffic signs in an image. The final application is to analyze video feed from a video camcorder placed in a vehicle. With focus placed on effective utilization of computer resources in order to achieve real time identification of signs in a video stream.
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Measurement of mechanical quantities with the support of CCD cameras
Marek, Petr ; Richter, Miloslav (referee) ; Havlíková, Marie (advisor)
This master’s work deals with types of digital cameras used in industry. Furthermore, there are mentioned application areas of cameras in the automotive industry. There was created searches industrial cameras on the market. In the next part is described hardware interconnection of Basler acA 1300-60gc camera and measuring platform cDAQ 9178. Part of this work is to create a system for synchronization of camera and measured data. In the last part is developed program for image processing and determine the angle of rotation of the steering wheel.
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Image processing using Android device - gas-meter value recognition
Wertheim, Michal ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
This thesis describes the design of the image processing for Android system, consisting of the choice of the development environment and its implementation. Workflow solution to the problem involves development of the Androidapplication and it’s graphical user interface. The text includes description of the application functionality, communicationwith a camera, storing and retrieving data. It also describes used algorithms and image processing methods used for detecting values from the counter of the gas meter.
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3D reconstruction
Musálek, Martin ; Horák, Karel (referee) ; Richter, Miloslav (advisor)
Thesis solves 3D reconstruction of an object by method of lighting by pattern. A projector lights the measured object by defined pattern and two cameras are measuring 2D points from it. The pedestal of obejct rotates and during the measure are acquired data from different angles. Points are indentified from measured images, transformed to 3D using stereovision, connected to 3D model and displayed.
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Image processing using Android device
Hortai, František ; Petyovský, Petr (referee) ; Honec, Peter (advisor)
This thesis describes the design and workflow of creating an image processing application in Android system, and what are the possibilities in choosing development environment and how to implement them. Then I am writing about my solutions of creating applications, graphical user interface and an interface for Android. I am describing my approach in the design and functionality of the application, communication with the camera, storing and retrieving data. Further I explain which algorithms were implemented for image processing and image evaluation. Product of this thesis is a functioning application that allows to its user to capture images and video stream. The algorithm evaluates the entering data and shows the location of the number plate. The application also allows recognizing texts and numbers from images. There are other various practical features and options implemented within the application.
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Optical character recognition from image data
Marinič, Michal ; Uher, Václav (referee) ; Burget, Radim (advisor)
The thesis is concerned with optical character recognition from image data with different methods used for character classification. In the first theoretical part it focuses on explanation of all important parts of system for optical character recognition. The latter practical part of the thesis describes an example of image segmentation, the implementation of artificial neural networks for image recognition and create simple training set of data for the evaluation of the network. It also describes the process of training Tesseract tool and its implementation in a simple application EasyTessOCR for character recognition.
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Image object detection using template
Novák, Pavel ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This Thesis is focused to Image Object Detection using Template. Main Benefit of this Work is a new Method for sympthoms extraction from Histogram of Oriented Gradients using set of Comparators. In this used Work Methods of Image comparing and Sympthoms extraction are described. Main Part is given to Histogram of Oriented Gradients Method. We came out from this Method. In this Work is used small training Data Set (100 pcs.) verified by X-Validation, followed by tests on real Sceneries. Achieved success Rate using X-Validation is 98%. for SVM Algorithm.
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