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Vplyv značky na nákupné rozhodovanie spotrebiteľa – porovnanie vnímania privátnej a tradičnej značky zo strany spotřebiteľa na slovenskom a českom trhu
Bátorová, Monika
The thesis deals with the impact of brands on consumer purchasing decisions and compares the perception of a private and traditional brand by the consumer on the Slovak and Czech markets. The main objective of this thesis is to identify factors of consumer behaviour and to specify consumer attitudes, consumer preferences in relation to private and traditional food labels, on the basis of an analysis of the brand's impact on consumer purchasing decisions on the Slovak and Czech markets. The results of the questionnaire survey (n=837) were used for quantitative research. Based on the experience gained in relation to the identified factors influencing consumer behaviour and attitudes, consumer preferences were denied by segmentation. Subsequently, quantitative research was supplemented by qualitative form of individual in-depth interviews (n=12) for individual segments. On the basis of these studies, recommendations were proposed for interest groups and individual segments on the Slovak and Czech markets.
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Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
Nagyová, Miriam ; Nohel, Michal
This paper focuses on training a deep learning model for vertebral body segmentation of the lumbar spine. The nnU-Net model was trained and tested on a publicly available dataset LumVBCanSeg consisting of 185 lumbar CT scans. Dice coefficient was used to evaluate the accuracy of the trained model. The mean Dice coefficient of the testing dataset was 0.949 with a standard deviation of 0.103. The model was also tested on clinical data containing various abnormalities, such as lytic lesions in multiple myeloma patients and metallic implants. Results were evaluated visually. While the model showed high accuracy on the testing dataset, the results on scans with anomalies showed a decline in accuracy.
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Implementation of a deep learning model for segmentation of multiple myeloma in CT data
Gálík, Pavel ; Nohel, Michal
This paper deals with the implementation of a deep learning model for spinal tumor segmentation of multiple myeloma patients in CT data. Deep learning is becoming an important part of developing computer-aided detection and diagnosis systems. In this study, a database of 25 patients who were imaged on spectral CT and for whom different parametric images (conventional CT, virtual monoenergetic images, calcium suppression images) were reconstructed, was used. Three convolutional neural network models based on the nnU-Net framework for lytic lesion segmentation were trained on the selected data. The results were evaluated on a test database and the trained models were compared.
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Segmentation of arterial wall in high resolution retinal images
Polachová, Natálie ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
This thesis focuses on automatic segmentation of retinal arterial walls in images acquired using adaptive optics. Adaptive optics is a non-invasive imaging method that provides high lateral resolution and allows detailed observation of retinal microstructures, including arterial walls. This technology is crucial for early diagnosis of serious diseases such as arterial hypertension and diabetic retinopathy. The main objective of this work was to detect the arterial lumen and segment its walls. Morphological and filtration techniques were used for lumen detection. For arterial wall segmentation, brightness profiles along the detected lumen were analyzed and active contour and spline methods were used. The results show that the active contour segmentation method improves the accuracy of arterial wall detection, especially in high-contrast regions. This thesis summarizes the findings and proposes improvements in the detection of the inner side of the arterial wall, which reduces the segmentation success rate in this work.
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Segmentation of important structures in retinal images
Trojánek, Václav ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
This bachelor thesis focuses on the segmentation of significant structures in retinal image data to improve the diagnosis and treatment of ocular diseases. Methods of retinal image analysis are investigated and implemented in this thesis. The thesis begins with an overview of the anatomy of the eye and the principles of background eye imaging using a fundus camera and an experimental video ophthalmoscope. This is followed by a detailed literature search focusing on current methods for the detection and segmentation of diagnostically important structures such as the optic disc, macula and blood vessels. A key part of the work is the implementation and testing of selected algorithms, including Hough transform for optic disc detection and OTSU thresholding for blood vessel segmentation and yellow spot detection based on previous optic disc segmentation.
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Conversion of fingerprints captured by a mobile device into a standardized format - image editing
Mucha, Vojtěch ; Říha, Kamil (referee) ; Číka, Petr (advisor)
This bachelor thesis deals with the issue of fingerprint conversion taken by a mobile device into a standardized format. In the present day, mobile devices are used more and more often to acquire biometric data, fingerprints included. Processing and standardization of such data is an essential part of the subsequent biometric analysis. The aim of the work is to design and implement an algorithm which would convert a photo of a finger into a grey scale picture of its fingerprint with distinct papillary lines and subdued valleys. The algorithm is implemented in C++ using OpenCV library and a trained neural network for finger detection from hand image. The achieved results are evaluated according to the algorithms for assessing the quality of fingerprints NFIQ 2 and Innovatrics.
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Market segmentation using statistical methods
Bystřická, Michaela ; Marciánová, Pavla (referee) ; Schüller, David (advisor)
The thesis is focused on the segmentation of customers of selected summer swimming pool. The first part of the thesis is devoted to the theoretical concept of the chosen issue. In the analytical part, a summer swimming pool is presented and selected analyses are carried out. The analytical part also includes a questionnaire survey. In the last part of the thesis, measures are proposed that would lead to an increase in the level of services for the selected customer segment.
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Road and path segmentation in images for autonomous driving scenario
Janíček, Ondřej ; Cihlář, Miloš (referee) ; Svědiroh, Stanislav (advisor)
This bachelor's thesis deals with the topic of segmentation of roads and paths for the purposes of autonomous driving. In the theoretical part, it deals with computer vision, simple segmentation methods, and practical solutions to the problem using convolutional neural networks and classical methods. In the practical part, the work deals with the collection of test data, the selection of a suitable programming language, and the selection of suitable libraries. Subsequently, the procedure for programming our own solution will be presented. Here it starts with pre-processing to convert the image into a grayscale image and filtering the noise, then finding the edges in the image using the Canny edge detector, followed by the definition of the region of interest, with the subsequent Hough transform to detect the straight lines in the image, and in the last stage, filtering the horizontal lines and averaging the remaining lines. At the end of the thesis, the results of the presented solution are compared with respect to robustness and computational complexity.
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