No description available.

This thesis describes the anatomical characteristics of fingerprints and their applications in identifying the person. The theoretical part describes the importance of papillary lines on fingerprints, statistical analysis and pre-processing of images in particular. The practical section provides the necessary operations to compare fingerprints. The implementation was done in Matlab.

As an effort to improve accessibility to historical documents, digitization of historical archives has been an ongoing process at many institutions since the origination of Optical Character Recognition. The old, scanned documents can contain deteriorations acquired over time or caused by old printing methods. Common visual attributes seen on the documents are variations in style and font, broken characters, ink intensity, noise levels and damage caused by folding or ripping and more. Many of these attributes are disfavoring for modern Optical Character Recognition tools and can lead to failed character recognition. This study approaches stated problem by using image processing methods to improve the result of character recognition. Furthermore, common image quality characteristics of scanned historical documents with unidentifiable text are analyzed. The Optical Character Recognition tool used to conduct this research was the open-source Tesseract software. Image processing methods like Gaussian lowpass filtering, Otsu’s optimum thresholding method and morphological operations were used to prepare the historical documents for Tesseract. Using the Precision and Recall classification method, the OCR output was evaluated, and it was seen that the recall improved by 63 percentage points and the precision by 18 percentage points. This shows that using image pre-processing methods as an approach to increase the readability of historical documents for Optical Character Recognition tools is effective. Further it was seen that common characteristics that are especially disadvantageous for Tesseract are font deviations, occurrence of non-belonging objects, character fading, broken characters, and Poisson noise.

This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.

Tato práce se zabývá multimodální registrací snímků sítnice z různých skenovacích zařízení. Multimodální registrace umožňuje zvýraznit prvky na snímcích sítnice, které jsou důležité pro detekci různých typů onemocnění oka (jako je glaukom, degradace nervových vláken, degradace cév, atd.). Teoretická část tvoří zhruba první půlku práce a je následována praktickou částí, která popisuje postupy při různých typech registrací snímků z fundus kamery, SLO a OCT. Registrace fundus a SLO snímků je provedena pomocí prostorové transformace. Tato práce popisuje tři různé metody registrace SLO snímků se snímky z fundus kamery. První a zároveň nejjednodušší je manuální registrace. Druhou je automatická registrace založená na metodě korelace. Výsledky, včetně porovnání obou metod, jsou uvedeny v závěru. Třetím typem je poloautomatická registrace, která využívá výhod obou předchozích metod a tím pádem je kompromisem mezi rychlostí a přesností registrace. Registrace fundus snímků a B-scanů z OCT je realizována dvěma různými metodami. První je opět založená na korelaci a druhá na prostorové transformaci. Všechny tyto registrační metody jsou realizovány také prakticky v programovém prostředí Matlab.

This master's thesis is engaged in machine vision, methods of image processing and analysis. The reason is to create application to determine relative positions of Trilobot robots in the laboratory.

No description available.

This project aims to develop an automated image pre-processing chain to extract valuable information from appliance labels before recycling. The primary goal is to improve optical character recognition accuracy by addressing noise issues using reinforcement learning and an evolutionary algorithm. Python was selected as the primary programming language for this project due to its extensive support for machine learning and computer vision libraries. Different techniques are implemented to enhance text extraction from labels. Binary Robust Invariant Scalable Keypoints (BRISK) are used to straighten labels and separate the label from the background. You Only Look Once version 8x (YOLOv8x) is then used for extracting the regions containing the text of interest. The reinforcement learning model and genetic algorithm dataset are created using BRISK with YOLOv8x. The results showed that pre-processing images in the dataset, provided through BRISK and YOLOv8x, does not affect text extraction accuracy, as suggested by reinforcement learning and evolutionary algorithms. / Detta projekt syftar till att utveckla en automatiserad bildförbehandlingskedja för att extrahera värdefull information från apparatmärken före återvinning. Det primära målet är att förbättra noggrannheten för optisk teckenigenkänning genom att hantera brusproblem med hjälp av förstärkningsinlärning och en evolutionär algoritm. Python valdes som det primära programmeringsspråket för detta projekt på grund av dess omfattande stöd för maskininlärnings- och datorseendebibliotek. Olika tekniker implementeras för att förbättra textutvinningen från etiketterna. Binary Robust Invariant Scalable Keypoints (BRISK) används för att räta ut etiketter och separera etiketten från bakgrunden. You Only Look Once version 8x (YOLOv8x) används sedan för att extrahera områden som innehåller den önskade texten. Datasetet för förstärkningsinlärningsmodellen och den genetiska algoritmen skapas genom att använda BRISK med YOLOv8x. Resultaten visade att förbehandlingen av bilder i datasetet, som tillhandahålls genom BRISK och YOLOv8x, inte påverkar noggrannheten för textutvinning, som föreslagits av förstärkningsinlärning och evolutionära algoritmer.

The global use of solar power is growing at an unprecedented rate, making the need toaccurately track the energy generation of decentralised solar energy systems (SES) more andmore relevant. The purpose of this thesis is to further develop a binary image classifier for thesimulation system framework known as Alfrödull, which will be used to detect and segment SESfrom aerial images to simulate the energy generation within a given Swedish municipality on anhourly basis. This project focuses on improving the Alfrödull classifier through four differentanalyses. the first focusing on examining how additional training data from publicly availabledatasets affects the model performance. The second on how the model can be improvedthrough the use of various image pre-processing techniques. The third on how the model canbe improved through balancing the training datasets to make up for the low amount of positiveimages as well as utilising model ensembles for joint classification. Finally, the fourth analysisemploys a sliding window approach to classify overlapping image tiles. The results show thathaving training data that is a good representation of the environment the model will be used in iscrucial, that the use of image augmentation policies can significantly improve modelperformance, that compensating for class imbalance as well as utilising ensemble methodspositively impacts model performance and that a sliding window approach to classifyingoverlapping images significantly decreases the amount of missed SES at the cost of clusters offalsely classified negative images (false positives). In conclusion, this thesis serves as animportant stepping stone in the practical implementation of the Alfrödull framework, showcasingthe key aspects in making a well performing binary image classifier of SES in Sweden.