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Structural Methods of Objects Identification for Industrial Robot Operation
Minařík, Martin ; Šlapal, Josef (referee) ; Konečný, Vladimír (referee) ; Šťastný, Jiří (advisor)
This PhD thesis deals with the use of structural methods of objects identification for industrial robots operation. First, the present state of knowledge in the field is described, i.e. the whole process of objects recognition with the aid of common methods of the syntactic analysis. The main disadvantage of these methods is that is impossible to recognize objects whose digitalized image is corrupted in some ways (due to excessive noise or image disturbances), objects are therefore deformed. Further, other methods for the recognition of deformed objects are described. These methods use structural description of objects for object recognition, i.e. methods which determine the distance between attribute descriptions of images. The core part of this PhD thesis begins in Chapter 5, where deformation grammars, capable of description of all possible object deformations, are described. The only complication in the analysis is the ambiguity of the deformation grammar, which lowers the effectiveness of the analysis. Further, PhD thesis deals with the selection and modification of a proper parser, which is able to analyze a deformation grammar effectively. Three parsers are described: the modified Earley parser, the modified Tomita parser and the modified hybrid LRE(k) parser. As for the modified Earley’s parser, ways of its effective implementation are described. One of the necessary parts of the object recognition is providing the invariances, which this PhD thesis covers in detail, too. Finally, the results of described algorithms are mentioned (successfulness and speed of deformed objects recognition) and suggested testing environment and implemented algorithms are described. In conclusion, all determined possibilities of deformation grammars and their results are summarized.
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Methods of Segmentation and Identification of Deformed Vertebrae in 3D CT Data of Oncological Patients
Jakubíček, Roman ; Flusser, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
In this doctoral thesis, the design of algorithms enabling the implementation of a fully automatic system for vertebrae segmentation in 3D computed tomography (CT) image data of possibly incomplete spines, in patients with bone metastases and vertebral compressions is presented. The proposed algorithm consists of several fundamental problems: spine detection and its axis determination, individual vertebra localization and identification (labeling), and finally, precise segmentation of vertebrae. The detection of the spine, specifically identifying its ends, and determining the course of the spinal canal, combines several advanced methods, including deep learning-based approaches. A novel growing circle method has been designed for tracing the spinal cord canal. Further, the innovative spatially variant filtering of brightness profiles along the spine axis leading to intervertebral disc localization has been proposed and implemented. The discs thus obtained are subsequently identified via comparing the tested vertebrae and model of vertebrae provided by a machine-learning process and optimized by dynamic programming. The final vertebrae segmentation is provided by the deformation of the complete-spine intensity model, utilizing a proposed multilevel registration technique. The complete proposed algorithm has been validated on testing databases, including also publicly available datasets. This way, it has been proven that the newly proposed algorithms provide results at least comparable to other author’s algorithms, and in some cases, even better. The main strengths of the algorithms lie in high reliability of the results and in the robustness to even strongly distorted vertebrae of oncological patients and to the occurrence of artifacts in data; moreover, they are capable of identifying the vertebra labels even in incomplete spinal CT scans. The strength is also in the complete automation of the processing and in its relatively low computational complexity enabling implementation on standard PC hardware. The system for fully automatic localization and labeling of distorted vertebrae in possibly incomplete spinal CT data is presented in this doctoral thesis. The design of algorithms enabling the implementation utilizes several novel approaches, which were presented at international conferences and published in the journal Jakubicek et al. (2020). Based on the results of the experimental validation, the proposed algorithms seem to be routinely usable and capable of providing fully acceptable input data (identified and precisely segmented vertebrae) as needed in the subsequent automatic spine bone lesion analysis.
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Pattern Recognition in Image Using Classifiers
Juránek, Roman ; Španěl, Michal (referee) ; Herout, Adam (advisor)
An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the method of construction of strong classifiers will be explained. WaldBoost extension to the algorithm will be described. The thesis deals with image features that are often used as element of weak classifiers. Brief introduction to pattern recognition in context of computer vision will be outlined in the begining of the work. Also some widely used methods of classifier training will be presented. An object detection library based on AdaBoost classifiers was developed as part of the work. The library was used in implementation of software that in praktice demonstrates object detection in videosquences. Last part of the work describes tool for training of AdaBoost classifiers.
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Eye recognition system
Zvak, Martin ; Richter, Miloslav (referee) ; Petyovský, Petr (advisor)
This work deals with biometric methods, aiming at possibilities of eye recognition, and their application. It contains overview of biometrics evolution such as independent, fully accepted science during last decades and nowadays. Also it contains overview of most expanded biometric methods and takes a look on chosen method of eye recognition – iris recognition. Next aim of this work is design and description of implementation of suitable identification method for iris recognition, based on simple practices of image processing.
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Barcode recognition system
Svoboda, Radovan ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
This work deals with principle and technological process used to detect barcodes that are commonly used. It contains brief overview of barcodes according to type and structure. Also it takes a closer look on type DATAMATRIX and its analysis. It describes possible hardware and software approaches that can be used to detect barcodes, describes concrete concept of scanning barcode and software solution for its identification.
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Detection of Elliott Waves Using Neural Networks
Grega, Martin ; Vašíček, Zdeněk (referee) ; Minařík, Miloš (advisor)
This thesis deals with application of artificial neural networks for detection of Elliott waves in static time-series. It is focused mainly on use of simple committee machine consisting of multilayer perceptrons trained by resillient propagation algorithm. Thesis contains design and implementation of application for detection of impulsive waves on input signal.
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Applied Methods for Transparent Materials Inspection
Horák, Karel ; Honec, Jozef (advisor)
A lot of production lines contain camera inspection systems that increase quality of production. Therefore this presented work deals with applications of computer image processing methods in defectoscopy. Concretely the thesis is concerned with defects evaluation of glass bottles in food operations by the help of visual system BTCAM612, which is in existing configuration installed inland and in several foreign countries. The system is developed in conjunction with developer company CAMEA Ltd. from Brno and it is its sole ownership. The whole process of bottles inspection is described in sequence. First of all it is the hardware acquisition of images of three main controlled parts of bottles – neck, bottom and side. Next chapters are concentrated on image processing and features classification. The features are obtained from image by methods based on detection of in-homogeneities on glass material. Essential part of work is focused on filtration of synthetic patterns from bottles bottoms using function of complex invariants. These patterns are occurred especially in many plants in eastern countries, where marketplace with inspection systems and generally with quality inspection of industrial lines is expanded lately.
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Detection of Logopaedic Defects in Speech
Pešek, Milan ; Smékal, Zdeněk (referee) ; Atassi, Hicham (advisor)
The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
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Generic Flow Analysis in Computer Networks
Jančová, Markéta ; Holkovič, Martin (referee) ; Kolář, Dušan (advisor)
Tato práce se zabývá problematikou popisu síťového provozu pomocí automaticky vytvořeného modelu komunikace. Hlavním zaměřením jsou komunikace v řídicích systémech , které využívají speciální protokoly, jako je například IEC 60870-5-104 . V této práci představujeme metodu charakteristiky síťového provozu z pohledu obsahu komunikace i chování v čase. Tato metoda k popisu využívá deterministické konečné automaty , prefixové stromy a analýzu opakovatelnosti. Ve druhé části této diplomové práce se zaměřujeme na implementaci programu, který je schopný na základě takového modelu komunikace verifikovat síťový provoz v reálném čase.
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