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Robot Localization Using OpenStreet Map
Rajnoch, Zdeněk ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
Goal of this thesis is localization of mobile robot in OpenStreet map segment. Robot IMU, odometry and compass sensors are used for trajectory reconstruction, which is compared to reference GPS trajectory. Extended Monte Carlo localization and clusterization are used for robot localization. Software is implemented in C++ with ROS middleware.
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Localisation of Mobile Robot in the Environment
Němec, Lukáš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This paper addresses the problem of mobile robot localization based on current 2D and 3D data and previous records. Focusing on practical loop detection in the trajectory of a robot. The objective of this work was to evaluate current methods of image processing and depth data for issues of localization in environment. This work uses Bag of Words for 2D data and environment of point cloud with Viewpoint Feature Histogram for 3D data. Designed system was implemented and evaluated.
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Automatic People Counting from Panoramic Photography
Blucha, Ondřej ; Kolář, Martin (referee) ; Veľas, Martin (advisor)
This bachelor thesis deals with automatic people counting from panoramic photography. This is very useful for counting large number of people, such as on the stadium or on the concerts. It consists of the two parts. The first one is image stitching, which process the images by the feature-based methods. The second part is people counting using face detection, where were used Viola-Jones detector. The ideal setting of parameters for used methods was experimentally selected.
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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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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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Object Detection Using Kinect
Němec, Lukáš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This paper address the problem of object recognition using Microsoft Kinect in the fi eld of computer vision. The objective of this work was to evaluate current methods of detection of objects using depth map (RGB-D sensor). The work deals with the enviroment of point cloud and Viewpoint Feature method. It also describes the use of binary classifi er in the context of object recognition. Object detection was implemented and performed experiments with it.
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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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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Automatic Content-Based Image Categorization
Němec, Ladislav ; Španěl, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with automatic content-based image classification. The main goal of this work is implementation of application which is able to perform this task automatically. The solution consists of variable system using local image features extraction and visual vocabulary built by k-means method. Bag Of Words representation is used as a global feature describing each image. Support Vector Machines - the final component of this system - perform the classification based on this representation. In the last chapter, the results of this experimental system are presented.
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Sheet Music Recognition and Playing from Photography
Staněk, Jiří ; Veľas, Martin (referee) ; Pavelková, Alena (advisor)
This work deals with development of an application for optical music recognition. This application is designed for mobile phones with Android operating system. The work includes a brief introduction to the problem and introduces some existing solutions. There are described methods for image processing and classification, which are used in final application. It also shows the design and implemenation of the final aplication, where used methods for detection and removal of staff lines, detection and processing of musical symbols and their classification are described. The evaluation of the final application and a summary of achieved results are shown in the end of this work.
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