Positioning system for 3D scans inside objects

En este trabajo presentamos un sistema de posicionamiento de visión activa para el escaneo 3D del interior de piezas. El diseño del sistema propuesto consta de dos módulos: un sistema de dimensionamiento 2D de visión activa, y un sistema que posiciona el módulo de visión activa. El sistema de posicionamiento es capaz de determinar la profundidad del sistema de dimensionamiento 2D de visión activa en el interior del objeto a escanear usando varios sensores. Las principales contribuciones de este trabajo son la caracterización del sistema de dimensionamiento 2D, y el desarrollo de algoritmos de posicionamiento de la luz activa con énfasis en el modelado y fusión de sensores. El sistema puede utilizarse como un sistema de dimensionamiento en aplicaciones industriales como la industria metal mecánica, la aeronáutica, la medicina, en el control de calidad y en áreas de visión por computadora.In this work we present an active positioning system for 3D scan of interior parts. The design of the proposed system consists of two modules: an active 2D dimensional system and positional system based on active vision. The active 2D dimensional system is able to determine the depth of the 2D dimensional system inside the object to be scanned using several sensors. The main contributions of this work are the characterization of the 2D dimensional system and the development of active light positioning algorithms with emphasis on the modeling and fusion of the sensors. The system can be used as a dimensional system in industrial applications such as the metal mechanical industry, aeronautics industry, medicine, quality control and computer vision.Peer Reviewe

Calibration of Robot’s Omnidirectional Vision System

Käesolev töö käsitles ühte tehisnägemise arendamisega seotud probleemidest - kuidas luua seos robotile läbi kaamera laekuva pildi ja reaalse maailma vahel. Seose loomiseks on vajalik kaamera kalibreerimine testpildi põhjal. Uuriti võimalust sellise programmi kirjutamiseks, mis on võimeline genereeritud piltide põhjal esitama teisenduse pildi ja mingi kindla reaalse maailma tasandi vahel. Ülesande lahendamiseks on mitmeid üldisi võimalusi, mis põhinevad enamasti kaamera parameetrite hindamisel. Töös käsitletud lähenemine eeldab, et soovitakse kalibreerida mingi kindla tasandi suhtes ruumis. Lahenduse üldidee seisnes ühe valge ruudu kasutamises kaamera kalibreerimiseks. Kõigepealt tuli kalibratsiooniruut tuvastada. Seejärel rakendati teadmist, et füüsilises ruumis oli kalibratsioonimuster ruut. Observeeritava ruumi ja füüsilise ruumi vahelise teisenduse leidmisel kasutati bilineaarset seost. Võrrandite rohkuse tõttu rakendati kiireima laskumise meetodit, et minimiseerida kõigi küljepikkuste vigasid. Lahenduskäiku rakendava programmi kirjutamiseni ei jõutud. Töös kirjeldati mitmeid varemloodud või praegu arenduses olevaid kaamera kalibratsiooni tööriistu. Kirjeldustes toodi välja tööriistade vahelisi seoseid ja eripärasid. Õpiti kasutama POVRay tarkvarapaketti katseandmete simuleerimiseks, lisaks sellele süvendati programmeerimiskeele Python kohta käivaid teadmisi. Implementeeriti proof of consept tüüpi programm, mis kasutab modifitseeritud versiooni W. Sun [41] poolt kirjeldatud kihilisest nurgatuvastusmeetodist. Esitati viis pildi kalibreerimiseks vajalikust bilineaarsest teisendusest koos näidislahendusega üksikute kaadrite juhul. Töö edasiarendamiseks tuleks kirjeldatud kalibratsioonimeetod implementeerida ja seejärel seda vastavalt reaalse maailma katsetulemustele optimeerida.The aim of this paper is to present a method for camera calibration. The actual implementation of the calibration process itself is not included in this paper, the solution is only theoretical. The camera calibration is considered in this paper in the meaning of spatial calibration and not in the sense of photometric camera calibration. The calibration problem arose in the robotics competition Robotex. The exact location of objects on a certain plane needs to be estimated by the robot in real-time. This means that there is a need for mapping between pixels and real world distances. This paper presents a set of existing methods for solving this camera calibration problem. The test data is generated using POVRay ray-tracing software. This enables predictable test cases for the software. A white square sheet of paper is used as the calibration pattern. A simplified version of W. Sun’s [41] corner detection algorithm is implemented to extract the location of the calibration pattern from an image. After the corners have been extracted, a method based on bilinear interpolation is proposed to calibrate the camera. The information that the calibration pattern is a square in the physical world is used in the calibration method. The proposed method suggests that using more frames increases the accuracy of the calibration. In order to improve the accuracy, the image is divided into subsections that are assumed to have a bilinear transformation from the physical world to the observed image. The next research in this field should implement the suggested method to verify it’s accuracy

Non-parametric Models of Distortion in Imaging Systems.

Traditional radial lens distortion models are based on the physical construction of lenses. However, manufacturing defects and physical shock often cause the actual observed distortion to be different from what can be modeled by the physically motivated models. In this work, we initially propose a Gaussian process radial distortion model as an alternative to the physically motivated models. The non-parametric nature of this model helps implicitly select the right model complexity, whereas for traditional distortion models one must perform explicit model selection to decide the right parametric complexity. Next, we forego the radial distortion assumption and present a completely non-parametric, mathematically motivated distortion model based on locally-weighted homographies. The separation from an underlying physical model allows this model to capture arbitrary sources of distortion. We then apply this fully non-parametric distortion model to a zoom lens, where the distortion complexity can vary across zoom levels and the lens exhibits noticeable non-radial distortion. Through our experiments and evaluation, we show that the proposed models are as accurate as the traditional parametric models at characterizing radial distortion while flexibly capturing non-radial distortion if present in the imaging system.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120690/1/rpradeep_1.pd

Automatic Camera Calibration Applied to Medical Endoscopy

International audienceThe paper proposes a new calibration algorithm for cameras with lens distortion, that uses a single image of a planar chessboard pattern acquired in general position. The radial distortion is modeled using the first order division model, and the method provides a closed form estimation of the intrinsic parameters and distortion coefficient. The experimental evaluation shows that the calibration accuracy is comparable to state-of-the-art algorithms requiring multiple input images. We believe that our approach is particularly well suited for the the calibration of medical endoscopes in computer aided surgery. Since the lens is mounted on the camera before each usage in the OR, the calibration procedure must be performed by the clinical practitioner with minimum effort. We solve this problem by proposing a fully automatic procedure that requires no human intervention other than acquiring a single calibration image

Konzeption und Entwicklung eines trinokularen Endoskops zur robusten Oberflächenerfassung in der minimalinvasiven Chirurgie

Die minimalinvasive Chirurgie ist eine besonders anspruchsvolle Aufgabe für den Chirurgen, da die Operation ausschließlich über Endoskope und stangenartige, filigrane Instrumente erfolgt. Computerassistierte Stereo-Endoskopiesysteme erleichtern die Tiefenwahrnehmung und unterstützen bei verschiedensten Anwendungen wie z.B. der Resektion eines Nierentumors durch Augmented Reality. Eine wesentliche Aufgabe ist die robuste dreidimensionale Erfassung der beobachteten Oberfläche der Organe. Aufgrund starker Reflexionen durch die endoskopische Lichtquelle, homogener Texturen und weicher, sich bewegender Geometrien ist eine zuverlässige Oberflächenerfassung sehr herausfordernd und stellt noch ein ungelöstes Problem dar. In dieser Arbeit wird deshalb ein neuartiges miniaturisiertes Dreikamerasystem als Demonstrator für ein trinokulares Endoskop sowie ein Algorithmus zur Dreibildauswertung mit semi-globaler Optimierung entwickelt. Durch synthetische und reale Messdaten werden theoretische Überlegungen anhand von drei Hypothesen geprüft. Im Vergleich zu einer stereoskopischen Auswertung wird untersucht, ob eine Dreibildauswertung robustere Ergebnisse liefert, kleinere Referenz- und Suchfenster ermöglicht und eine rechenzeitaufwendige semi-globale Optimierung ersetzt. Es stellt sich heraus, dass die ersten beiden Annahmen grundsätzlich zutreffen, eine semi-globale Optimierung aber nur bedingt ersetzt werden kann. Weiterhin werden die Fehlereinflüsse durch Reflexionen näher spezifiziert und durch gekreuzte Polarisationsfilter sehr effektiv unterdrückt. Das vorgestellte Dreikamera-Endoskop und angepasste Auswerteverfahren tragen wesentlich zur Verbesserung der computerassistierten Endoskopie bei und bringen die Forschungen in diesem Gebiet einen Schritt voran.Minimally invasive surgery is a quite challenging task to the surgeon due to operation through an endoscope and sensitive telescopic instruments exclusively. Computer assisted stereo endoscopic systems eases depth perception and supports several tasks such as dissection of a renal tumour by augmented reality. An essential procedure is robust surface reconstruction of the observed organs. Due to strong reflections from the endoscopic light source, homogeneous textures and weak deforming geometries robust surface reconstruction becomes quite challenging and is not solved successfully yet. Therefore, in this work a novel miniaturised three camera endoscope is introduced and an algorithm for three image analysis and semi-global optimisation is implemented. Synthetic and real experimental measurements are conducted to evaluate theoretical assumptions and review three hypotheses. In contrast to stereo analysis, it is examined whether three image analysis leads to more robust results, allows for smaller matching window sizes and replaces a time-consuming semiglobal matching algorithm. The investigations show that the first two assumptions can generally be confirmed, but the semi-global matching is necessary in some cases. Additionally, errors by reflections are examined in more detail and are suppressed efficiently by crossed polarising filters. The novel three camera endoscope and customized image analysis algorithm gives a great benefit to computer assisted endoscopy and brings research a step closer to more reliable assistant systems