Global ETD Search

151	Rozpoznávání žil prstů lidské ruky / Recognition of Finger Veins of a Human Hand Szalayová, Lucia January 2010 (has links) This diploma thesis deals with different ways of grayscale image processing of veins in a finger of human hand. The process consists of vein structure extraction, and then this structure is compared with collection of provided snapshots. First of all we introduced problem of biometric systems and image processing. There are presented also some commercial solutions from the same field. Within the detailed description of image processing we suggested different modifications in adaptive thresholding algorithm, feature extraction, which continues into comparison of vein structures. Thesis is closed by testing and final review.
152	Embedded zpracování videa pro dohledový systém / Embedded video processing for surveillance systems Árva, Gábor January 2017 (has links) Diplomová práca sa zaoberá návrhom embedded dohľadového systému, ktoré je implementované na Raspberry Pi 3 B zariadenie. Uvedený systém obsahuje algoritmy pre detekcie pohybu a detekcie objektov, ktoré sú realizované pomocou OpenCV funkcie. Vyhodnocené informácie sú prístupné na webový server.
153	Measuring the Speed of a Floorball Shot Using Trajectory Detection and Distance Estimation With a Smartphone Camera : Using OpenCV and Computer Vision on an iPhone to Detect the Speed of a Floorball Shot / Mätning av hastigheten på ett innebandyskott genom detektering av projektilbana och avståndsbedömning med kameran i en smartphone Schmidt, Eric January 2016 (has links) This thesis describes the possibilities of using smartphones and their cameras in combination with modern computer vision algorithms to track and measure the speed of a floorball. Previous research within the area is described and an explanation is given as to why an implementation using three-frame temporal differencing to detect objects in motion works best to detect and track the ball. 100 floorball shots were recorded and measured using a speedometer radar and two different smartphones, one running the application and the other recording each shot. The video recording for each shot was then used to manually create a baseline for speed comparison. A second experiment was later conducted to analyse the sensitivity and effect on the determined ball size in the floorball shot analysis. The results from the first experiment show that the speedometer radar results in average deviate by 12% from the speed baseline. The speedshooting application however has results that, on average, deviate from the speed baseline by 6%. Furthermore, the results show that a faulty ball size detection is the major cause of error in the speedshooting application. The main conclusion that can be drawn from this is that it is possible to use a smartphone and computer vision methodologies to determine the speed of a floorball shot. In fact, it is even possible to do so with greater accuracy than the radar used in the experiments in this thesis. However, to prove the accuracy of the application for normal use, further testing needs to be conducted in new experiment conditions, for example by recording shots at higher speeds than those recorded in the experiments in this thesis. Computer Vision Floorball Bontouch Ball Trajectory Speed Motion Detection Distance Estimation Smartphone Camera OpenCV iPhone iOS Computer Sciences Datavetenskap (datalogi)
154	Accelerating Reverse Engineering Image Processing Using FPGA Harris, Matthew Joshua 10 May 2019 (has links) No description available. Computer Engineering FPGA OpenCV Image Stitching image matching reverse engineering multimodal matching hardware Trojans hardware Trojan detection integrated circuit
155	Padel court detection system Wennerblom, David, Arronet, Andrey January 2023 (has links) The aim of this thesis is to examine the possibility of a court detection program for sports videos that can identify the court even when some important elements are not visible. The study will also analyze what external factors may impact the program's accuracy in detecting all relevant elements. These questions are answered through a combination of computer vision techniques and algorithms. The study utilizes Design Science Research (DSR) as its research methodology to develop an artifact. A dataset of padel sports videos are evaluated to measure the artifacts accuracy. The artifact utilizes multiple computer vision techniques from the OpenCV library to detect relevant lines and edges and project them onto the frame using a predetermined court model as reference. The findings indicated that the developed artifact demonstrated a relatively consistent level of accuracy in court detection across multiple courts, whenever a detection was made. However, the frequency of successful detections exhibited some inconsistency. The research also found that external factors did not significantly influence the accuracy of court detection, yet they posed challenges to the program's overall consistency. Court detection Computer vision Image processing Padel Hough lines Canny edge detector Homography OpenCV Computer Systems Datorsystem
156	A Comparison of Monocular Camera Calibration Techniques Van Hook, Richard L. 23 May 2014 (has links) No description available. Scientific Imaging Computer Engineering Computer Science Optics Calibration Reprojection Error Visual Structure from Motion OpenCV calibration patterns
157	A New Way for Mapping Texture onto 3D Face Model Xiang, Changsheng January 2015 (has links) No description available. Computer Engineering Computer Science Electrical Engineering texture mapping 3D face model mesh parametrization image processing OpenGL OpenCV
158	Improve Nano-Cube Detection Performance Using A Method of Separate Training of Sample Subsets Nagavelli, Sai Krishnanand January 2016 (has links) No description available. Computer Science Integral image Cascade classifier Adaboost algorithm Transmission electron microscopy Rapid HAAR feature OpenCV Image detection
159	Entwicklung einer offenen Softwareplattform für Visual Servoing Sprößig, Sören 29 June 2010 (has links) (PDF) Ziel dieser Diplomarbeit ist es, eine flexibel zu verwendende Plattform für Visual Servoing-Aufgaben zu Erstellen, mit der eine Vielzahl von verschiedenen Anwendungsfällen abgedeckt werden kann. Kernaufgabe der Arbeit ist es dabei, verschiedene Verfahren der Gesichtserkennung (face detection) am Beispiel der Haar-Kaskade und -wiedererkennung (face recognition) am Beispiel von Eigenfaces und Fisherfaces zu betrachten und an ausführlichen Beispielen vorzustellen. Dabei sollen allgemeine Grundbegriffe der Bildverarbeitung und bereits bekannte Verfahren vorgestellt und ihre Implementierung im Detail dargestellt werden. Aus den dadurch gewonnen Erkenntnissen und dem sich ergebenden Anforderungsprofil an die zu entwickelnde Plattform leitet sich anschließend die Realisierung als eigenständige Anwendung ab. Hierbei ist weiterhin zu untersuchen, wie die neu zu entwickelnde Software zukunftssicher und in Hinblick auf einen möglichen Einsatz in Praktika einfach zu verwenden realisiert werden kann. Sämtliche während der Arbeit entstandenen Programme und Quellcodes werden auf einem separaten Datenträger zur Verfügung gestellt. Eine komplett funktionsfähige Entwicklungsumgebung wird als virtuelle Maschine beigelegt. AdaBoost Boosting C# C/C++ Eigenfaces Eigengesichter Emgu Fisherfaces Gesichtserkennung Gesichtswiedererkennung Haar-Kaskade LDA Matlab OpenCV PCA face detection face recognition ddc:500 ddc:004 Bildverarbeitung Programmierumgebung Programmierung
160	Near touch interactions: understanding grab and release actions. Balali Moghaddam, Aras 17 August 2012 (has links) In this work, I present empirically validated techniques to realize gesture and touch interaction using a novel near touch tracking system. This study focuses on identifying the intended center of action for grab and release gestures close to an interactive surface. Results of this experiment inform a linear model that can approximate the intended location of grab and release actions with an accuracy of R^2 = 0.95 for horizontal position and R^2 = 0.84 for vertical position. I also present an approach for distinguishing which hand was used to perform the interaction. These empirical model data and near touch tracking system contributions provide new opportunities for natural and intuitive hand interactions with computing surfaces. / Graduate Human Factors near touch multi touch hand interaction interactive surfaces user study hand tracking hardware prototype bimanual opencv action center gesture center hand center

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