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Automatic Photography Categorization
Matuszek, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deal with choosing methods, design and implementation of application, which is able of automatic categorization photos based on its content into predetermined groups. Main steps of categorization are described in greater detail. Finding and description of interesting points in image is implemented using SURF, creation of visual dictionary by k-means, mapping on the words through kd-tree structure. Own evaluation is made for categorization. It is described, how the selected steps were implemented with OpenCV and Qt libraries. And the results of runs of application with different settings are shown. And efforts to improve outcome, when the application can categorize right, but success is variable.
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Automatic Photography Categorization
Veľas, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.
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Interest-Point Detection on CUDA
Ryba, Jan ; Řezníček, Ivo (referee) ; Herout, Adam (advisor)
Corner point detection is one of many functions used in computer vision for tasks such as tracking, detecting objects, comparing images and much more. Many of the algorithms are complex and require a lot of CPU time. This is where the CUDA platform comes in. CUDA kernels run parallely on graphic accelerators can rapidly decrease time needed for execution, allowing even these complex calculations to work in real time or even better. Text focuces on Moravec and Harris corner detection algorithms and their effective implementation on CUDA. Examination of potetntial and performance of CUDA platform is also importatnt.
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Searching for Points of Interest in Raster Image
Kaněčka, Petr ; Sumec, Stanislav (referee) ; Herout, Adam (advisor)
This document deals with an image points of interest detection possibilities, especially corner detectors. Many applications which are interested in computer vision needs these points as their necessary step in the image processing. It describes the reasons why it is so useful to find these points and shows some basic methods to find them. There are compared features of these methods at the end.
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Detection of Corresponding Points in Images
Komosný, Petr ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This thesis is interested in detection of corresponding points in images, which display the same object, eventually some of important elements and synchronizing these images. The aim of this thesis is to find, study and choose suitable algorithm for detecting interesting points in image. This algorithm will be apply at couple of images and in these images will find couples of corresponding points across these images. Functional output of this thesis will be application which will realize choosen interesting points detector, algorithm for finding correspondencies of regions and their synchronizing and joint them to one output image.
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3D Scene Reconstruction from Images
Hejl, Zdeněk ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis describes methods of reconstruction of 3D scenes from photographs and videos using the Structure from motion approach. A new software capable of automatic reconstruction of point clouds and polygonal models from common images and videos was implemented based on these methods. The software uses variety of existing and custom solutions and clearly links them into one easily executable application. The reconstruction consists of feature point detection, pairwise matching, Bundle adjustment, stereoscopic algorithms and polygon model creation from point cloud using PCL library. Program is based on Bundler and PMVS. Poisson surface reconstruction algorithm, as well as simple triangulation and own reconstruction method based on plane segmentation were used for polygonal model creation.
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Eye Tracking in User Interfaces
Jurzykowski, Michal ; Beran, Vítězslav (referee) ; Zemčík, Pavel (advisor)
Tato diplomová práce byla vytvořena během studijního pobytu na Uviversity of Estern Finland, Joensuu, Finsko. Tato diplomová práce se zabývá využitím technologie sledování pohledu neboli také sledování pohybu očí (Eye-Tracking) pro interakci člověk-počítač (Human-Computer Interaction (HCI)). Navržený a realizovaný systém mapuje pozici bodu pohledu/zájmu (the point of gaze), která odpovídá souřadnicím v souřadnicovém systému kamery scény do souřadnicového systému displeje. Zároveň tento systém kompenzuje pohyby uživatele a tím odstraňuje jeden z hlavních problémů využití sledování pohledu v HCI. Toho je dosaženo díky stanovení transformace mezi projektivním prostorem scény a projektivním prostorem displeje. Za použití význačných bodů (interesting points), které jsou nalezeny a popsány pomocí metody SURF, vyhledání a spárování korespondujících bodů a vypočítání homografie. Systém byl testován s využitím testovacích bodů, které byly rozložené po celé ploše displeje.
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Automatic Photography Categorization
Veľas, Martin ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.
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Similarity Measure of Points of Interest in Image
Křehlík, Jan ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This document deals with experimental verifying to use machine learning algorithms AdaBoost or WaldBoost to make classifier, that is able to find point in the second picture that matches original point in the first picture. This work also depicts finding points of interest in image as a first step of finding correspondence. Next there are described some descriptors of points of interest. Corresponding points could be useful for 3D modeling of shooted scene.
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3D Objects Detection in Images
Bordovský, Gabriel ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This bachelors thesis deals with detection of a known 3D object in images and its pose estimation. The method uses the ORB-type keypoints and their location on the surface of a bounding box. By using solve of PnP problem a pose of the object is obtained using the points 2D coordinates from the image and the 3D coordinates of the very points from the registered model. This thesis expands a detection method for simple box-shaped objects, which is a part of OpenCV library, for the usage on more complex objects. In experiments, the detector reached a detection success rate of 85 % and the computed pose matches the real one approximately for 88 %.
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