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Impact of color models on performance of convolutional neural networks
Šimunský, Martin ; Doležel, Petr (referee) ; Škrabánek, Pavel (advisor)
Current knowledge about impact of colour models on performance of convolutional neural network is investigated in the first part of this thesis. The experiment based on obtained knowledge is conducted in the second part. Six colour models HSV, CIE 1931 XYZ, CIE 1976 L*a*b*, YIQ a YCbCr and deep convolutional neural network ResNet-101 are used. RGB colour model achieved the highest classification accuracy, whereas HSV color model has the lowest accuracy in this experiment.
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Object detection in video using neural networks and Android application
Mikulec, Vojtěch ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This master’s thesis deals with the implementation of functional solution for classifying road users using mobile device with Android operating system. The goal is to create Android application which classifies vehicles in real time using rear-facing camera and saves timestamps of classification. Testing is performed mostly with own, diversely modificated dataset. Five models are trained and their performance is measured in dependence on hardware. The best classification performance is from pretrained MobileNet model where transfer learning with 6 classes of own dataset is used – 62,33 %. The results are summarized and a method for faster and more accurate traffic analysis is proposed.
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Design of learning and equipment module using AI on Raspberry PI and Intel Movidius platform
Macko, Tomáš ; Richter, Miloslav (referee) ; Janáková, Ilona (advisor)
This bachelors thesis describes the process of implementing trained neural network model to AI accelerator - Intel Movidius. The first chapter is about machine learning and computer vision theory. The second chapter describes the options which can be chosen for programming of convolutional neural networks as programming language or related libraries which suit the most. The third and fourth chapters are highly connected. They describe the whole process of hardware installation and troubleshooting of software issues during installation. The next chapter shows previews of images, which are used as data input for neural network. Next pages describe used scripts and models of neural networks which were created from scratch. The last chapters are all about measured datas during the training or testing of neural networks and its evaluation.
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Food classification using deep neural networks
Kuvik, Michal ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.
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Deep Learning for Image Classification
Ziková, Jana ; Veľas, Martin (referee) ; Hradiš, Michal (advisor)
This bachelor thesis deals with electronic commerce website products classification using product's photographs. For this purpose we use already implemented models of deep convolutional neural networks. Tho goal of this theses is to design experiments that will lead to the best possible results in product images classification.
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Anatomy based landmark detection in brain CT scans
Krajčiová, Alexandra ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
Manual detection of anatomical landmarks from head CT (Computed Tomography) scans is time-consuming task prone to observer errors. In addition, the accuracy of the detection correlates with image quality. The aim of this work is to create an algorithm that will perform automatic detection of anatomical landmarks. These landmarks can be later used to form radiological lines, which finds its application in CT scanning. SVM (Support Vector Machines) and HOG (Histograms of Oriented Gradients) features was chosen for anatomical landmark detection. The achieved results, possibilities of further progress and improvement of detection are summarized in the conclusion.
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Segmentation Methods in Biomedical Image Processing
Mikulka, Jan ; Přibil, Jiří (referee) ; Dostál, Otto (referee) ; Gescheidtová, Eva (advisor)
The PhD thesis deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR) and microscopic images of tissues. It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown in this thesis. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. The results of the thesis are methods proposed for automatic image segmentation and classification.
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Detecting the Occurrence of Objects in a Video
Šamánek, Jan ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This bachelor thesis deals with detection of objects in videos by using primarily convolution neural networks and creating simple user interface, which allows user to choose classification model and use it to analyze video or train given model on own dataset. First part is dedicated to description of machine learning and neural networks. After that follows the section about image description and image classification using machine learning algorithms and data augmentation. Last part deals with describtion of own design of neural network and user interface and describing achieved results.
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Using structural method for objects recognition
Valsa, Vít ; Heriban, Pavel (referee) ; Šťastný, Jiří (advisor)
This diploma thesis deals with posibilities of using structural methods for recognition objects in a picture. The first part of this thesis describes methods for preparing the picture before processing. The core of the whole thesis is in chapter 3, where is analyzed in details the problem of the formation of deformation grammars for parsing and their using. In the next part is space for syntactic parser describing the deformation grammar. The conclusion is focused on testing the suggested methods and their results.
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The effect of the background and dataset size on training of neural networks for image classification
Mikulec, Vojtěch ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis deals with the impact of background and database size on training of neural networks for image classification. The work describes techniques of image processing using convolutional neural networks and the influence of background (noise) and database size on training. The work proposes methods which can be used to achieve faster and more accurate training process of convolutional neural networks. A binary classification of Labeled Faces in the Wild dataset is selected where the background is modified with color change or cropping for each experiment. The size of dataset is crucial for training convolutional neural networks, there are experiments with the size of training set in this work, which simulate a real problem with the lack of data when training convolutional neural networks for image classification.
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