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Height Measurement in Digital Image
Olejár, Adam ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
The aim of this paper is a summary of the theory necessary for a modification, detection of person and the height calculation of the detected person in the image. These information were then used for implementation of the algoritm. The first half reveals teoretical problems and solutions. Shows the basic methods of image preprocessing and discusses the basic concepts of plane and projective geometry and transformations. Then describes the distortion, that brings into the picture imperfections of optical systems of cameras and the possibilities of removing them. Explains HOG algorithm and the actual method of calculating height of person detected in the image. The second half describes algoritm structure and statistical evaluation.
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Support of Mapping by Image Processing
Jaroš, Ján ; Herman, David (referee) ; Váňa, Jan (advisor)
This bachelor's thesis deals with methods of detection of selected objects in video and with importing these objects into OpenStreetMap central database based on their geographic location. It focuses mainly on recognition of road signs. First section briefly describes some of the most widely used methods and OpenStreetMap project itself. In the following chapters is given a more detailed overview of used methods of proposed system, its implementation and testing. The conclusion contains evaluation of whole work and the possible improvements are listed here.
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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Multi Object Class Learning and Detection in Image
Chrápek, David ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
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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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Gender Recognition from Photograph
Kałuża, Marian ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This paper presents a multiresolution approach for gender recognition based on Histogram of Oriented Gradients and Local Binary Patterns. The experiment showed that gender recog- nition accuracy can be improved not only by aquiring different features on the same image resolution but even by gathering just a single feature at different image scales. The pre- sented approach is quite competitive with above 95% accuracy in both evaluated datasets.
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Road Sign Detection from Camera in Car
Dušek, Jan ; Sochor, Jakub (referee) ; Beran, Vítězslav (advisor)
This bachelor's thesis is focused on detection of traffic signs from image or video. Algorithms common for object detection will be introduced in the beginning. Description of object detection using histogram of oriented gradients and support vector machines will follow. Last part will present accomplished results.
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Detection of racist symbols in pictures
Klapal, Matěj ; Říha, Kamil (referee) ; Povoda, Lukáš (advisor)
Goal of this thesis is detector of racist symbols from the picture using functions from the open source library OpenCV. Text also summarizes description of basic processes of image processing via computers. This text contains descriptions of some methods from the library allowing us to train and afterwards detect and localize requested object. This text also compares accuracy of detection using Haar-like features, Local Binary Patterns (LBP) and histogram of oriented gradients. Text also summarizes results of a test of detection for three supported symbols, swastika, signs of SS and triskelion.
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Detection, Localization and Recognition of Traffic Signs
Svoboda, Tomáš ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This master's thesis deals with the localization, detection and recognition of traffic signs. The possibilities of selection of areas with possible traffic signs occurrence are analysed. The properties of different kinds of features used for traffic signs recognition are described next. It focuses on the features based on histogram of oriented gradients. Some possible classifiers are discussed, in the first place the cascade of support vector machines, which are used in resulting system. A description of the system implementation and data sets for 5 types of traffic signs is part of this thesis. Many experiments were accomplished with created system. The results of the experiments are very good. New datasets were acquired from approximately 9 hours of processed video sequences. There are about 13 500 images in these datasets.
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