Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Computer Vision Aided Print Pattern Generation in Inkjet Printed Electronics

Inkjet printed electronics is one of the new promising electronics manufacturing techniques out there. It has become a widely adopted manufacturing method especially in the field of low-cost electronics. This thesis considers an application of inkjet printed electronics where conductive ink is used for printing the connections between the components on a single unit called a module. The base module is fabricated by molding the components together such that the connection points of the components form a level surface. After this, the wiring is printed on top. Because of the inaccuracies in the fabrication process, there is often a mismatch between the designed print pattern and the target module. The purpose of this thesis is to introduce an online print pattern generation system that uses computer vision to detect the locations of the module components and then modifies the print pattern accordingly. By integrating the print pattern generation system as a part of the manufacturing process, not only is it possible to print functioning modules but also multiple modules can be printed at the same time. This way the capabilities of inkjet printed electronics can be more efficiently harnessed. The experiments prove that the developed print pattern correction system together with the proposed imaging setup are able to produce desired results in practice. In addition to successfully printing ten modules at once, it is also shown that the developed system is robust and generalizes well for different types of modules. /Kir1

Reconstruction of moving surfaces of revolution from sparse 3-D measurements using a stereo camera and structured light

Das Ziel dieser Arbeit ist die Entwicklung und Analyse der algorithmischen Methodik zur Rekonstruktion eines parametrischen Oberflächenmodells für ein rotationssymmetrisches Objekt aus einer Sequenz von dünnen 3D-Punktwolken. Dabei kommt ein neuartiges Messsystem mit großem Sichtfeld zum Einsatz, das auch in schwierigen Bedingungen eingesetzt werden kann. Das zu vermessende Objekt kann während der Aufnahme der Sequenz einer als analytisches Modell formulierbaren Bewegung unterliegen. Das Verfahren wird anhand einer praktischen Anwendung zur Oberflächenrückgewinnung eines Rades analysiert und entwickelt. Es wird gezeigt, dass die durch Fit eines einfachen Models für jede Einzelmessung erzielbare Genauigkeit durch Anpassung eines globalen Modells unter gleichzeitiger Einbeziehung aller Einzelmessungen und unter Berücksichtigung eines geeigneten Bewegungsmodells erheblich verbessert werden kann. Die Gewinnung der dreidimensionalen Punktdaten erfolgt mit einem Stereokamerasystem in Verbindung mit aktiver Beleuchtung in Form eines Punktmusters. Eine relativ hohe Punktdichte im gesamten Sichtfeld des Stereokamerasystems wird durch Verbindung mehrerer Laserprojektoren zu einer Projektionseinheit erzielt. Durch exakte Kalibrierung des Kamerasystems und der Projektionseinheit wird trotz großer Streuung der Laserpunkte im Kamerabild unter Ausnutzung der trifokalen geometrischen Bedingungen eine hohe Genauigkeit in den dreidimensionalen Punktdaten erzielt

Model-based human upper body tracking using interest points in real-time video

Vision-based human motion analysis has received huge attention from researchers because of the number of applications, such as automated surveillance, video indexing, human machine interaction, traffic monitoring, and vehicle navigation. However, it contains several open problems. To date, despite very promising proposed approaches, no explicit solution has been found to solve these open problems efficiently. In this regard, this thesis presents a model-based human upper body pose estimation and tracking system using interest points (IPs) in real-time video. In the first stage, we propose a novel IP-based background-subtraction algorithm to segment the foreground IPs of each frame from the background ones. Afterwards, the foreground IPs of any two consecutive frames are matched to each other using a dynamic hybrid localspatial IP matching algorithm, proposed in this research. The IP matching algorithm starts by using the local feature descriptors of the IPs to find an initial set of possible matches. Then two filtering steps are applied to the results to increase the precision by deleting the mismatched pairs. To improve the recall, a spatial matching process is applied to the remaining unmatched points. Finally, a two-stage hierarchical-global model-based pose estimation and tracking algorithm based on Particle Swarm Optimiation (PSO) is proposed to track the human upper body through consecutive frames. Given the pose and the foreground IPs in the previous frame and the matched points in the current frame, the proposed PSO-based pose estimation and tracking algorithm estimates the current pose hierarchically by minimizing the discrepancy between the hypothesized pose and the real matched observed points in the first stage. Then a global PSO is applied to the pose estimated by the first stage to do a consistency check and pose refinement