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31	Matching handwritten notes using computer vision and pattern matching Åslund, Conrad January 2022 (has links) What people take for granted is not as easy for computers. Being able tojudge whether an image is the same even though it has a differentresolution or is taken from a different angle or light condition is easyfor humans but much more difficult for computers. Today’s mobiles aremore powerful than ever, which has opened up for more hardware-demandingalgorithms to be processed. How to effectively match handwritten notesto eliminate duplicates in an application. Are there better or worsemethods and approaches, and how do they compare to each other? Can youachieve both accuracy and speed? By analyzing images taken at differentangles, distances, and lighting conditions, different methods andapproaches have been developed and analyzed. The methods are representedin various tables where time and accuracy are represented. Eightdifferent methods were evaluated. The methods were tuned on one datasetconsisting of 150 post-it notes, each imaged under four conditions,leading to 600 images and 1800 possible pair-wise matches. The methodswere thereafter evaluated on an independent dataset consisting of 250post-it notes, each imaged under four conditions, leading to 1000 imagesand 3000 possible pair-wise matches. The best method found 99.7%, andthe worst method found 62.9% of the matching pairs. Seven of the eightevaluated matches did not make any incorrect matches. / Det människor tar för givet är inte lika lätt för datorer. Att kunna bedöma om en bild är den samma fast den har annan upplösning eller är tagen från en annan vinkel eller ljusförhållande är lätt för människor men betydligt svårare för datorer. Dagens mobiler är kraftfullare än någonsin vilket har öppnat upp för att mer hårdvaru krävande algoritmer kan processas. Hur matchar man handskrivna lappar på ett effektivt sätt för att eliminera kopior i en applikation. Finns det bättre eller sämre metoder och tillvägagångssätt, och hur står de gentemot varandra? Kan man uppnå både träffsäkerhet samt snabbhet? Genom att analysera bilder tagna får olika vinklar, avstånd samt ljusförhållanden har olika metoder och tillvägagångssätt utvecklats och analyserats. Metoderna är representerade i olika tabeller där tid, samt träffsäkerhet redovisas. Åtta olika metoder utvärderades. Metoderna ställdes in på ett dataset bestående av 150 post-it-lappar, var och en fotograferade under fyra förhållanden, vilket ledde till 600 bilder och 1800 möjliga matchningar. Metoderna utvärderades därefter på ett oberoende dataset bestående av 250 post-it-lappar, var och en fotograferade under fyra förhållanden, vilket ledde till 1000 bilder och 3000 möjliga matchningar. Den bästa metoden fann 99,7% och den sämsta metoden hittade 62,9% av de matchande paren. Sju av de åtta utvärderade metoderna gjorde inga felaktiga matchningar. Computer vision SURF ORB Feature detection matching images
32	The Synthetic spider silk fibers spun from Pyriform Spidroin 2, a glue silk protein discovered in orb-weaving spider attachment discs Geurts, Paul 01 January 2010 (has links) Spider attachrnentdisc silk fibers are spun into a viscous liquid that rapidly solidifies, gluing dragline silk fibers to substrates for locomotion or web construction. Here we report the identification and artificial spinning of a novel attachment disc glue silk fibroin, Pyriform Spidroin 2 (PySp2), from the golden orb weaver Nephila c/avipes. MS studies support PySp2 is a constituent of the pyriform gland that is spun into attachment discs. Analysis of the PySp2 protein architecture reveals sequence divergence relative to the other silk family members, including the cob weaver glue silk fibroin PySpl. PySp2 contains internal block repeats that consist of two sub-repeat units: one dominated by Ser, Gin and Ala, the other Pro-rich. Artificial spinning of recombinant PySp2 truncations shows that the Ser-Gln-Ala-rich sub-repeat is sufficient for the assembly of polymeric subunits and subsequent fiber formation. These studies support that both orb- and cob-weaving spiders have evolved highly polar block-repeat sequence with the ability to self-assemble into fibers, suggesting a strategy to allow fiber fabrication in the liquid environment of the attachment discs. Spider webs DNA Analysis Orb weavers Proteins Research Textile fibers Synthetic North America Textile fibers Synthetic Biology Life Sciences
33	Systém pro autonomní mapování závodní dráhy / System for autonomous racetrack mapping Soboňa, Tomáš January 2021 (has links) The focus of this thesis is to theoretically design, describe, implement and verify thefunctionality of the selected concept for race track mapping. The theoretical part ofthe thesis describes the ORB-SLAM2 algorithm for vehicle localization. It then furtherdescribes the format of the map - occupancy grid and the method of its creation. Suchmap should be in a suitable format for use by other trajectory planning systems. Severalcameras, as well as computer units, are described in this part, and based on parametersand tests, the most suitable ones are selected. The thesis also proposes the architectureof the mapping system, it describes the individual units that make up the system, aswell as what is exchanged between the units, and in what format the system output issent. The individual parts of the system are first tested separately and subsequently thesystem is tested as a whole. Finally, the achieved results are evaluated as well as thepossibilities for further expansion.
34	Distribuovaný informační systém malé firmy / Distributed Information System for a Small Firm Pajgrt, Ondřej January 2010 (has links) This thesis deals with implementation of distributed information systém for a small construction engineering firm all the way from design to deployment. We will be introduced to distributed applications problems and technologies involved either throught direct relevance or as a support tool for the implementation of the project. In addition, this work will guide us throught complete design and implementation of a final product.
35	Rolling shutter in feature-based Visual-SLAM : Robustness through rectification in a wearable and monocular context Norée Palm, Caspar January 2023 (has links) This thesis analyzes the impact of and implements compensation for rolling shutter distortions in the state-of-the-art feature-based visual SLAM system ORB-SLAM3. The compensation method involves rectifying the detected features, and the evaluation was conducted on the "Rolling-Shutter Visual-Inertial Odometry Dataset" from TUM, which comprises of ten sequences recorded with side-by-side synchronized global and rolling shutter cameras in a single room. The performance of ORB-SLAM3 on rolling shutter without the implemented rectification algorithms substantially decreased in terms of accuracy and robustness. The global shutter camera achieved centimeter or even sub-centimeter accuracy, while the rolling shutter camera's accuracy could reach the decimeter range in the more challenging sequences. Also, specific individual executions using a rolling shutter camera could not track the trajectory effectively, indicating a degradation in robustness. The effects of rolling shutter in inertial ORB-SLAM3 were even more pronounced with higher trajectory errors and outright failure to track in some sequences. This was the case even though using inertial measurements with the global shutter camera resulted in better accuracy and robustness compared to the non-inertial case. The rectification algorithms implemented in this thesis yielded significant accuracy increases of up to a 7x relative improvement for the non-inertial case, which turned trajectory errors back to the centimeter scale from the decimeter one for the more challenging sequences. For the inertial case, the rectification scheme was even more crucial. It resulted in better trajectory accuracies, better than the non-inertial case for the less challenging sequences, and made tracking possible for the more challenging ones. SLAM rolling shutter rectification ORB-SLAM3 monocular camera compensation visual odometry simultaneous localization mapping
36	Vision based control and landing of Micro aerial vehicles / Visionsbaserad styrning och landning av drönare Karlsson, Christoffer January 2019 (has links) This bachelors thesis presents a vision based control system for the quadrotor aerial vehicle,Crazy ie 2.0, developed by Bitcraze AB. The main goal of this thesis is to design andimplement an o-board control system based on visual input, in order to control the positionand orientation of the vehicle with respect to a single ducial marker. By integrating a cameraand wireless video transmitter onto the MAV platform, we are able to achieve autonomousnavigation and landing in relatively close proximity to the dedicated target location.The control system was developed in the programming language Python and all processing ofthe vision-data take place on an o-board computer. This thesis describes the methods usedfor developing and implementing the control system and a number of experiments have beencarried out in order to determine the performance of the overall vision control system. Withthe proposed method of using ducial markers for calculating the control demands for thequadrotor, we are able to achieve autonomous targeted landing within a radius of 10centimetres away from the target location. / I detta examensarbete presenteras ett visionsbaserat kontrollsystem for dronaren Crazy ie 2.0som har utvecklats av Bitcraze AB. Malet med detta arbete ar att utforma och implementeraett externt kontrollsystem baserat pa data som inhamtas av en kamera for att reglera fordonetsposition och riktning med avseende pa en markor placerad i synfaltet av kameran. Genom attintegrera kameran tillsammans med en tradlos videosandare pa plattformen, visar vi i dennaavhandling att det ar mojligt att astadkomma autonom navigering och landning i narheten avmarkoren.Kontrollsystemet utvecklades i programmeringsspraket Python och all processering avvisions-datan sker pa en extern dator. Metoderna som anvands for att utvecklakontrollsystemet och som beskrivs i denna rapport har testats under ett ertal experiment somvisar pa hur val systemet kan detektera markoren och hur val de olika ingaendekomponenterna samspelar for att kunna utfora den autonoma styrningen. Genom den metodsom presenteras i den har rapporten for att berakna styrsignalerna till dronaren med hjalp avvisuell data, visar vi att det ar mojligt att astadkomma autonom styrning och landning motmalet inom en radie av 10 centimeter. vision control system quadcopter python camera vision based landning crazyflie optical visual odometry odometry transformation ArUco Kalman filter PID marker rigid transformations drone transform control system homogenous transformation ORB detection marker detection Drönare kontroll system kontrollsystem quadcopter quadrocopter kamera vision visionsbaserad styrning landning crazyflie optisk transform homogen transform ORB detektering markör Elektroteknik och elektronik
37	Automatická klasifikace obrazů / Automatic image classification Ševčík, Zdeněk January 2020 (has links) The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.
38	Pořizování vysoce kvalitních snímků rovinných povrchů chytrým telefonem / Capturing Very High Quality Images of Planar Surfaces by a Smartphone Masaryk, Adam January 2021 (has links) The aim of this thesis is to create a mobile application for Android, which allows users to create high-quality photos of planar objects. User can create multiple photographs of a selected planar object. These photographs are then aligned and combined into one final image. Various shortcomings that can be present in the photographs are filtered.
39	Detektory a deskriptory oblastí v obrazu / Region Detectors and Descriptors in Image Žilka, Filip January 2016 (has links) This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.
40	Comparison of Deep Learning and Feature Matching Methods For Homography Estimation David Karl Niblick (7908791) 25 November 2019 (has links) <div> Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods using Convolutional Neural Networks (CNNs) that are meant to replace the more traditional feature-matching based approaches to the estimation of homography. Our evaluation of the CNN based methods focuses particularly on measuring the performance under conditions of significant noise, illumination shift, and occlusion. We also measure the benefits of training CNNs to varying degrees of noise. Additionally, we compare the effect of using color images instead of grayscale images for inputs to CNNs. Finally, we compare the results against baseline feature-matching based homography estimation methods using SIFT, SURF, and ORB. We find that CNNs can be trained to be more robust against noise, but at a small cost to accuracy in the noiseless case. Additionally, CNNs perform significantly better in conditions of extreme variance than their feature-matching based counterparts. With regard to color inputs, we conclude that with no change in the CNN architecture to take advantage of the additional information in the color planes, the difference in performance using color inputs or grayscale inputs is negligible. About the CNNs trained with noise-corrupted inputs, we show that training a CNN to a specific magnitude of noise leads to a ``Goldilocks Zone'' with regard to the noise levels where that CNN performs best.</div> Computer Engineering homography computer vision convolutional neural networks deep learning feature matching SIFT SURF ORB noise Deep Image Homography Estimation

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