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Location-aware data transfers scheduling for distributed virtual walkthrough applications.
Přibyl, Jaroslav ; Sochor, Jiří (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
Data transfers scheduling process is an important part of almost all distributed virtual walkthrough applications. Its main purpose is to preserve data transfer efficiency and rendered image quality. The most limiting factors here are network restrictions. These restrictions can be reduced using multi-resolution data representation, download priority determination and data prefetching algorithms. Advanced priority determination and data prefetching methods use mathematic description of motion to predict next position of a user. These methods can accurately predict only close future positions. In the case of sudden but regular changes in user motion direction (road networks), these algorithms are not sufficient to predict future position with required accuracy and at required distance. In this thesis a systematic solution to data transfers scheduling is proposed which solves also these cases. The proposed solution uses next location prediction methods to compute download priority or additionally prefetch data needed to render a scene in advance. Experiments show that compared to motion functions the proposed scheduling scheme can increase data transfer efficiency and rendered image quality during exploration of tested scene.
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On-line Data Analysis Based on Visual Codebooks
Beran, Vítězslav ; Honec, Jozef (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This work introduces the new adaptable method for on-line video searching in real-time based on visual codebook. The new method addresses the high computational efficiency and retrieval performance when used on on-line data. The method originates in procedures utilized by static visual codebook techniques. These standard procedures are modified to be able to adapt to changing data. The procedures, that improve the new method adaptability, are dynamic inverse document frequency, adaptable visual codebook and flowing inverted index. The developed adaptable method was evaluated and the presented results show how the adaptable method outperforms the static approaches when evaluating on the video searching tasks. The new adaptable method is based on introduced flowing window concept that defines the ways of selection of data, both for system adaptation and for processing. Together with the concept, the mathematical background is defined to find the best configuration when applying the concept to some new method. The practical application of the adaptable method is particularly in the video processing systems where significant changes of the data domain, unknown in advance, is expected. The method is applicable in embedded systems monitoring and analyzing the broadcasted TV on-line signals in real-time.
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Design and Applications of Special-Purpose Two-Dimensional Visual Markers
Zachariáš, Michal ; Sojka, Eduard (oponent) ; Ftáčnik,, Milan (oponent) ; Herout, Adam (vedoucí práce)
Contemporary visual fiduciary marker systems have a major disadvantage compared to markerless approaches - the camera movement is tightly limited to the space where the markers are. At any frame of the camera image a marker must be large enough to provide sufficient amount of information for detection and identification and at the same time, it must be small enough to fit into the camera's field of view. These requirements are contradictory. This work presents a solution to this problem in a concept of the Marker Field. It is a structure whose presence can be detected in a camera image and the exact location within the field can be recognized based on just any sub-area of defined size. The sub-areas are not disjoint, but they are overlapping to a very large degree, to be identifiable from both close-up and distant views. Different implementations of the marker field concept are explained in this work, together with their intended uses and their advantages and disadvantages. To prove and support the usability of proposed marker fields, this work's second largest part discusses their several real-life applications.
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Vehicle Speed Measurement Using Stereo Camera Pair
Najman, Pavel ; Sojka, Eduard (oponent) ; Guillemaut, Jean-Yves (oponent) ; Zemčík, Pavel (vedoucí práce)
This thesis aims to answer the question whether it is currently possible to autonomously measure the speed of vehicles using a stereoscopic method with the average error within 1 km/h, the maximum error within 3 km/h, and the standard deviation within 1 km/h. The error ranges are based on the requirements of the OIML whose Recommendations serve as templates for metrological legislations of many countries. To answer this question, a~hypothesis is formulated and tested. A method that utilizes a stereo camera pair for vehicle speed measurement is proposed and experimentally evaluated. The experiments show that the technique overcomes state-of-the-art results with the mean error of approximately 0.05 km/h, the standard deviation of less than 0.20 km/h, and the maximum absolute error of less than 0.75 km/h. The results are within the required ranges, and therefore the formulated hypothesis holds.
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Scalable Multisensor 3D Reconstruction Framework
Šolony, Marek ; Kneip, Laurent (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
Realistic 3D models of the environment are beneficial in many fields, from natural or man-made structure inspection, robotic navigation and map building, to the movie industry, in particular, scene survey and special effects integration to scenes. It is common practice to capture the scene with multiple different types of sensors such as monocular, stereoscopic or spherical cameras or 360-degree laser scanners to achieve large coverage of the scene. The advantage of the laser scanners and spherical cameras is that they capture the full surrounding scene as a consistent seamless image. Using easy to operate and manipulate hand-held conventional cameras, the details of the scene obstructed areas are easily covered. The 3D reconstruction consists of three steps--data acquisition, data processing and registration, and refinement of the reconstruction. The contribution of this thesis is a careful analysis of the image registration from several types of cameras~(planar and spherical), as well as 3D laser measurements to obtain an initial estimation of the sensor position and the 3D structure. They are further refined by a unified representation system capable of integrating multisensor measurements and obtain an accurate 3D reconstruction of the environment. The evaluation of the multisensor 3D reconstruction is performed on multiple synthetic, and real-world datasets. The accuracy comparison with commercial multisensor 3D reconstruction software shows that our proposed solution achieves more accurate results. While the commercial solutions are limited to specific type of sensors, our framework can integrate any types of measurements and constraints.
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Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers
Polok, Lukáš ; Hartley, Richard (oponent) ; Sojka, Eduard (oponent) ; Smrž, Pavel (vedoucí práce)
This thesis focuses on data structures for sparse block matrices and the associated algorithms for performing linear algebra operations that I have developed. Sparse block matrices occur naturally in many key problems, such as Nonlinear Least Squares (NLS) on graphical models. NLS are used by e.g. Simultaneous Localization and Mapping (SLAM) in robotics, Bundle Adjustment (BA) or Structure from Motion (SfM) in computer vision. Sparse block matrices also occur when solving Finite Element Methods (FEMs) or Partial Differential Equations (PDEs) in physics simulations. The majority of the existing state of the art sparse linear algebra implementations use elementwise sparse matrices and only a small fraction of them support sparse block matrices. This is perhaps due to the complexity of sparse block formats which reduces computational efficiency, unless the blocks are very large. Some of the more specialized solvers in robotics and computer vision use sparse block matrices internally to reduce sparse matrix assembly costs, but finally end up converting such representation to an elementwise sparse matrix for the linear solver. Most of the existing sparse block matrix implementations focus only on a single operation, such as the matrix-vector product. The solution proposed in this thesis covers a broad range of functions: it includes efficient sparse block matrix assembly, matrix-vector and matrix-matrix products as well as triangular solving and Cholesky factorization. These operations can be used to construct both direct and iterative solvers as well as to compute eigenvalues. Highly efficient algorithms for both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are provided. The proposed solution is integrated in SLAM++ , a nonlinear least squares solver focused on robotics and computer vision. It is evaluated on standard datasets where it proves to significantly outperform other similar state of the art implementations, without sacrificing generality or accuracy in any way.
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Acceleration of Object Detection Using Classifiers
Juránek, Roman ; Kälviäinen, Heikki (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
Detection of objects in computer vision is a complex task. One of most popular and well explored approaches is the use of statistical classifiers and scanning windows. In this approach, classifiers learned by AdaBoost algorithm (or some modification) are often used as they achieve low error rates, high detection rates and they are suitable for detection in real-time applications. Object detection run-time which uses such classifiers can be implemented by various methods and properties of underlying architecture can be used for speed-up of the detection. For the purpose of acceleration, graphics hardware, multi-core architectures, SIMD or other means can be used. The detection is often implemented on programmable hardware. The contribution of this thesis is to introduce an optimization technique which enhances object detection performance with respect to an user defined cost function. The optimization balances computations of previously learned classifiers between two or more run-time implementations in order to minimize the cost function. The optimization method is verified on a basic example -- division of a classifier to a pre-processing unit implemented in FPGA, and a post-processing unit in standard PC.
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Image Restoration Based on Convolutional Neural Networks
Svoboda, Pavel ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
A merit of this thesis is to introduce a unified image restoration approach based on a convolutional neural network which is to some degree degradation type independent. Convolutional neural network models were trained for two different tasks, a motion deblurring of license plate images and a removal of artifacts related to lossy image compression. The capabilities of such models are studied from two main perspectives. Firstly, how well the model can restore an image compared to the state-of-the-art methods. Secondly, what is the model's ability to handle several ranges of the same degradation type. An idea of the unified end-to-end approach is based on a recent development of neural networks and related deep learning in a field of computer vision. The existing hand-engineered methods of image restoration are often highly specialized for a given degradation type and in fact, define state of the art in several image restoration tasks. The end-to-end approach allows to directly train the required model on specifically corrupted images, and, further, to restore various levels of corruption with a single model. For motion deblurring, the end-to-end mapping model derived from models used in computer vision is deployed. Compression artifacts are restored with similar end-to-end based model further enhanced using specialized objective functions together with a network skip architecture. A direct comparison of the convolutional network based models and engineered methods shows that the data-driven approach provides beyond state-of-the-art results with a high ability to generalize over different levels of degradations. Based on the achieved results, this work presents the convolutional neural network based methods suggesting a possibility having the unified approach used for wide range of image restoration tasks.
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HUMAN ACTION RECOGNITION IN VIDEO
Řezníček, Ivo ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This thesis focuses on the improvement of human action recognition systems. It reviews the state-of-the-art in the field of action recognition from video. It describes techniques of digital image and video capture, and explains computer representations of image and video. This thesis further describes how local feature vectors and local space-time feature vectors are used, and how captured data is prepared for further analysis, such as classification methods. This is typically done with video segments of arbitrarily varying length. The key contribution of this work explores the hypothesis that the analysis of different types of actions requires different segment lenghts to achieve optimal quality of recognition. An algorithm to find these optimal lengths is proposed, implemented, and tested. Using this algorithm, the hypothesis was experimentally proven. It was also shown that by finding the optimal length, the prediction and classification power of current algorithms is improved upon. Supporting experiments, results, and proposed exploitations of these findings are presented.
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Identifikace vozidel na snímcích dopravních situací
Petyovský, Petr ; Sojka, Eduard (oponent) ; Železný, Miloš (oponent) ; Horák, Karel (vedoucí práce)
Cílem této disertační práce je návrh metod pro získání dalších parametrů o vozidle ze snímků reálné dopravní situace ke stávající informaci o RZ vozidla a jeho poloze v měřeném úseku. Úkolem je využít stávající instalace kamerových systémů a na základě dat získaných z těchto zařízení navrhnout nové metody extrakce dalších parametrů o vozidle. Řešení lze rozdělit na dvě skupiny: 1. Metody pro získání příznaků a metody vyhodnocení dat, které povedou k rozpoznání typu vozidla na základě jediného snímku vozidla. 2. Metody pro získání údajů o tvaru vozidla na základě sekvence snímků projíždějícího vozidla
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