|
|
Visual detection of small objects using available tools in MATLAB
Sladký, Jiří ; Dobossy, Barnabás (referee) ; Appel, Martin (advisor)
This thesis investigates possibilities of small object detection in pictures using YOLO method, a deep learning algorithm available in MATLAB. In the thesis, a detector was designed and trained to detect cows from top-down view. A tool was created, that performs detection using the proposed model even on high resolution images and counts the present objects. A generator of synthetic images was programmed, which helped with training the model. Various experiments were performed that found the limits of YOLO and validated contribution of the proposed improvements.
|
|
|
Algorithms for improving the detection of selected cardiac arrhythmias
Šandová, Hana ; Ředina, Richard (referee) ; Novotná, Petra (advisor)
The work deals with the generation of ECG arrhythmias that are underrepresented in databases. The theoretical part of the thesis is devoted to a literature search of academic publications that deal with the classification of arrhythmia by using deep learning and data augmentation metod for ECG. The practical part of the thesis deals with noise generator, because adding noise to signals could make the dataset richer. Functions for augmentation of atrial flutter and 3rd and 2nd atrioventricular block were created. It has been tried generation of 2nd atrioventricular block using generative adversarial networks (GAN). Deep learning-based ECG classifiers were used for evaluating the efficiency of the proposed technique in generating synthetic ECG data.
|
|
|
Trainable Methods for Automatic Biomedical Image Processing
Uher, Václav ; Tučková,, Jana (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
This thesis deals with possibilities of automatic segmentation of biomedical images. For the 3D image segmentation, a deep learning method has been proposed. In the work problems of network design, memory optimization method and subsequent composition of the resulting image are solved. The uniqueness of the method lies in 3D image processing on a GPU in combination with augmentation of training data and preservation of the output size with the original image. This is achieved by dividing the image into smaller parts with the overlay and then folding to the original size. The functionality of the method is verified on the segmentation of human brain tissue on magnetic resonance imaging, where it overcomes human accuracy when compared a specialist vs. specialist, and cell segmentation on a slices of the Drosophila brain from an electron microscope, where published results from the impacted paper are overcome.
|
|
|
Image database expansion tool
Minařík, Jakub ; Honec, Peter (referee) ; Janáková, Ilona (advisor)
This thesis devotes to create a tool for expansion of image dataset. Thesis deals with theory of image processing and existing tools and the approaches in this field. From this teory, individual methods are designed and implemented. Futhermore, these methods are provided by user interface and batch start of altering images. At the end restrictive conditions of program are described.
|
|
|
Image-Based Clustering of Microbial Colonies
Láncoš, Jan ; Kišš, Martin (referee) ; Beneš, Karel (advisor)
Laboratorní analýza mikrobiálních kolonií pěstovaných na Petriho miskách je v současné době předmětem intenzivního výzkumu ve snaze o dosažení totální laboratorní automatizace. Jádro tohoto problému spočívá v přesné lokalizaci kolonií při obrazové analýze. Současná řešení nejčastěji využívají strojové učení, které je však obecně závislé na kvalitních datech, kterých je v tomto odvětví k dispozici jen velmi málo. Abych adresoval tento problém, vytvořil jsem víceúčelový generátor syntetických dat. Úspěšně jsem jej aplikoval jak při segmentaci tak při shlukování kolonií. Výsledné segmentační F1 skóre se mi podařilo navýšit z 0,518 na 0,729 a při shlukování jsem s využitím této segmentace dosáhl V-measure skóre 0,830. Prací navržený přístup ke generování syntetických dat nás posouvá o krok blíže k plné laboratorní automatizaci.
|
|
|
Keyboard and Keys Image Recognition
Lorenc, Jan ; Lichtner, Ondrej (referee) ; Pluskal, Jan (advisor)
Cílem práce je vytvoření řešení pro rozpoznání kláves na klávesnici za účelem automatizace robotického psaní na klávesnici. V rámci práce jsou vytvořeny datasety pro detekci klávesnice v obraze, rozpoznání znaků v obraze a dodatečnou korekci detekovaných znaků na základě různých rozložení klávesnic. Práce předkládá různé přístupy k řešení problému rozpoznání znaků na klávesnici a vybírá ten nejvhodnější. Navržený postup je rozdělen do 3 fází, kterým odpovídají připravené datasety. Pomocí neuronových sítí a Cannyho metody detekce hran se nejprve rozpozná klávesnice v obraze a následně se v nalezené klávesnici detekují jednotlivé znaky. V poslední fázi dochází k dodatečnému zpracování výsledků (oprava znaků, doplnění nerozpoznaných znaků, nalezení speciálních kláves apod.). Pro každou část jsou vyhodnoceny výsledky. Přínos práce spočívá ve vytvoření datasetů pro detekci klávesnice a jejích kláves a především modulárního a rozšiřitelného řešení pro detekční proces se slibnými výsledky.
|
|
|
Research of the new augmentation methods for online handwriting
Sigmund, Jan ; Burget, Radim (referee) ; Zvončák, Vojtěch (advisor)
Graphomotor difficulties of school-aged children are characterised by problems in handwriting and drawing and can lead to developmental dysgraphia. Timely clinical diagnosis is critical to provide preventive care. In practice however, it is not feasible on day-to-day basis due to the need for expert staff and the prevalence of difficulties up to 30\%. Machine learning models can serve as an accessible objective tool for evaluating graphomotor functioning. In most cases there is not enough data collected, which results in poor classification performance. Therefore, this thesis focuses on data augmentation of online handwriting. Generating artificial samples is based on recombination of intrinsic mode functions, obtained by empirical mode decomposition. IMFs of health controls, numbering 72, and with graphomotor difficulties, 94 children in total, are calculated. The decomposition is performed specifically on X and Y coordinate time series. IMFs of the same indices of different subjects are randomly interchanged, thus producing a new signal. Then, the graphomotor features of the original and artificial time series are extracted. Only the spatial ones related to the coordinates are selected. Finally, the correlations of the features of the two databases will be analyzed and compared.
|
|
|
Algorithms for improving the detection of selected cardiac arrhythmias
Šandová, Hana ; Ředina, Richard (referee) ; Novotná, Petra (advisor)
The work deals with the generation of ECG arrhythmias that are underrepresented in databases. The theoretical part of the thesis is devoted to a literature search of academic publications that deal with the classification of arrhythmia by using deep learning and data augmentation metod for ECG. The practical part of the thesis deals with noise generator, because adding noise to signals could make the dataset richer. Functions for augmentation of atrial flutter and 3rd and 2nd atrioventricular block were created. It has been tried generation of 2nd atrioventricular block using generative adversarial networks (GAN). Deep learning-based ECG classifiers were used for evaluating the efficiency of the proposed technique in generating synthetic ECG data.
|
|
|
Visual detection of small objects using available tools in MATLAB
Sladký, Jiří ; Dobossy, Barnabás (referee) ; Appel, Martin (advisor)
This thesis investigates possibilities of small object detection in pictures using YOLO method, a deep learning algorithm available in MATLAB. In the thesis, a detector was designed and trained to detect cows from top-down view. A tool was created, that performs detection using the proposed model even on high resolution images and counts the present objects. A generator of synthetic images was programmed, which helped with training the model. Various experiments were performed that found the limits of YOLO and validated contribution of the proposed improvements.
|
|
|
Image database expansion tool
Minařík, Jakub ; Honec, Peter (referee) ; Janáková, Ilona (advisor)
This thesis devotes to create a tool for expansion of image dataset. Thesis deals with theory of image processing and existing tools and the approaches in this field. From this teory, individual methods are designed and implemented. Futhermore, these methods are provided by user interface and batch start of altering images. At the end restrictive conditions of program are described.
|
guest ::
