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Image based flower recognition
Jedlička, František ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.
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Multichannel Image Deconvolution
Bradáč, Pavel ; Kolář, Radim (referee) ; Jiřík, Radovan (advisor)
This Master Thesis deals with image restoration using deconvolution. The terms introducing into deconvolution theory like two-dimensional signal, distortion model, noise and convolution are explained in the first part of thesis. The second part deals with deconvolution methods via utilization of the Bayes approach which is based on the probability principle. The third part is focused on the Alternating Minimization Algorithm for Multichannel Blind Deconvolution. At the end this algorithm is written in Matlab with utilization of the NAG C Library. Then comparison of different optimization methods follows (simplex, steepest descent, quasi-Newton), regularization forms (Tichonov, Total Variation) and other parameters used by this deconvolution algorithm.
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Canny's Operator and Other Useful Edge Detectors
Janda, Miloš ; Juránek, Roman (referee) ; Venera, Jiří (advisor)
This work introduces main approaches for digital image processing and defines fundamental terms for successful understanding. Main aim is description of several suitable methods used in digital image pre-processing, methods for edge detection and consequent post-processing of these. The final goal of this work is effective implementation and complex comparison of methods for edge detection.
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Longtime Video
Macháček, Martin ; Beran, Vítězslav (referee) ; Juránková, Markéta (advisor)
This thesis describes methods used for making time-lapse video from a set of photographs and tools, which make it easier. It suggests and evaluates different approaches useful for elimination of frames, which differ from the rest of the set, application of digital image filters and other useful features. Practical part of this work is a computer program implementing these methods and features.
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Techniques Used for Image Smoothing, Blurring and Sharpening
Kubínek, Jiří ; Šilhavá, Jana (referee) ; Venera, Jiří (advisor)
This work is dedicated to methods used for digital image editing. It defines fundamental terms for this work as color space or noise. Above all, it analyses methods allowing image sharpening and blurring. It describes some of the most known algorithms from the theoretical point of view, but also introduces their implementation in C programming language. There are compared according to time complexity. The purpose of this work is to introduce digital image filtering and demonstrate elementary procedures used for their implementation.
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Applications supporting lectures in image processing
Hlavatý, Jindřich ; Říha, Kamil (referee) ; Rajmic, Pavel (advisor)
The bachelor thesis deals with development of the programs for supporting of the tuition. The aim of my work is create to set of java applets. Introduction of my thesis explains concepts associated with this topic and necessary for solving this issue. Then I will take a closer look at color models and spaces. The conversion of color models to RGB models are described in this section. The following section shows decomposition into a bit level binary or using Gray code. Then, I will focus on image processing using convolution, conversion color image into gray scale image and dithering. In the end of my thesis the individual java applets are shown and described.
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Blood vessel segmentation in retinal image data
Vančurová, Johana ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
This master´s thesis deals with blood vessel segmentation in retinal image data. The theoretical part is focused on the basic description of anatomy and physiology of the eye and methods of observing the back of the eye. This thesis also describes the principles of classical and convolutional neural networks and segmentation techniques that are used to segment blood vessel in retinal images. In the practical part, a segmentation method using convolutional neural network U-net is implemented. This neural network is trained on the three datasets. Two datasets include images from experimental video ophthalmoscope. Because it impossible to compare the results of these two datasets with any other methods of retinal blood vessel segmentation, U-net is trained on other dataset that is HRF database. This dataset includes fundus images. The results of testing on this dataset serves for comparing results with other methods of retinal blood vessel segmentation.
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Image classification using deep learning
Hřebíček, Zdeněk ; Přinosil, Jiří (referee) ; Mašek, Jan (advisor)
This thesis deals with image object detection and its classification into classes. Classification is provided by models of framework for deep learning BVLC/Caffe. Object detection is provided by AlpacaDB/selectivesearch and belltailjp/selective_search_py algorithms. One of results of this thesis is modification and usage of deep convolutional neural network AlexNet in BVLC/Caffe framework. This model was trained with precision 51,75% for classification into 1 000 classes. Then it was modified and trained for classification into 20 classes with precision 75.50%. Contribution of this thesis is implementation of graphical interface for object detction and their classification into classes, which is implemented as aplication based on web server in Python language. Aplication integrates object detection algorithms mentioned abowe with classification with help of BVLC/Caffe. Resulting aplication can be used for both object detection (and classification) and for fast verification of any classification model of BVLC/Caffe. This aplication was published on server GitHub under license Apache 2.0 so it can be further implemented and used.
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