1,141 research outputs found
RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model
Accurate detection and localization of X-corner on both planar and non-planar
patterns is a core step in robotics and machine vision. However, previous works
could not make a good balance between accuracy and robustness, which are both
crucial criteria to evaluate the detectors performance. To address this
problem, in this paper we present a novel detection algorithm which can
maintain high sub-pixel precision on inputs under multiple interference, such
as lens distortion, extreme poses and noise. The whole algorithm, adopting a
coarse-to-fine strategy, contains a X-corner detection network and three
post-processing techniques to distinguish the correct corner candidates, as
well as a mixed sub-pixel refinement technique and an improved region growth
strategy to recover the checkerboard pattern partially visible or occluded
automatically. Evaluations on real and synthetic images indicate that the
presented algorithm has the higher detection rate, sub-pixel accuracy and
robustness than other commonly used methods. Finally, experiments of camera
calibration and pose estimation verify it can also get smaller re-projection
error in quantitative comparisons to the state-of-the-art.Comment: 15 pages, 8 figures and 4 tables. Unpublished further research and
experiments of Checkerboard corner detection network CCDN (arXiv:2302.05097)
and application exploration for robust camera calibration
(https://ieeexplore.ieee.org/abstract/document/9428389
Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications
This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads
Algorithms for Building High-Accurate Optical Tracking Systems
Die vorliegende Arbeit präsentiert eine Untersuchung von Einflussfaktoren auf die Genauigkeit eines optischen Trackingsystems zur hoch präzisen Koordinatenmessung, wie sie beispielsweise im Bereich der Computer-unterstützten Chirurgie benötigt wird. Zu den Haupteinflussfaktoren gehören die Modellierung der Aufnahmegeometrie, die verwendeten Bildverarbeitungsalgorithmen zur Markensegmentierung, welche sowohl während der Systemkalibrierung als auch während des eigentlichen Messvorgangs verwendet werden, und nicht zuletzt thermische Einflüsse.Wahrend die Modellierung der Kamerageometrie ein gut erforschter Gegenstand sowohl im Bereich der Photogrammetrie als auch des Maschinellen Sehens darstellt, existieren fur den Vergleich von verschiedenen Markentypen und deren Segmentierungsalgorithmen in bezug auf die Messgenauigkeit noch keine umfassenden Ergebnisse. Einen weiteren Bereich, der nahezu nicht untersucht ist, bilden thermische Einflüsse auf die zugrundeliegende Aufnahmegeometrie. Die vorliegende Arbeit legt ihren Schwerpunkt auf diese zwei Bereiche. Zum einen werden verschiedene Algorithmen zur Segmentierung von Messmarken vorgestellt und miteinander verglichen. Den zweiten großen Schwerpunkt bildet eine Analyse von thermischen Einflussen auf Kameras. Es wird ein Verfahren entwickelt, welches den Einfluss von Temperaturänderungen modelliert und so Messfehler kompensieren kann. Die Ergebnisse dieser Arbeit finden Anwendung in der Entwicklung eines optischen Trackingsystems fur den Einsatz in der orthopädischen Chirurgie
Asynchronous, Photometric Feature Tracking using Events and Frames
We present a method that leverages the complementarity of event cameras and
standard cameras to track visual features with low-latency. Event cameras are
novel sensors that output pixel-level brightness changes, called "events". They
offer significant advantages over standard cameras, namely a very high dynamic
range, no motion blur, and a latency in the order of microseconds. However,
because the same scene pattern can produce different events depending on the
motion direction, establishing event correspondences across time is
challenging. By contrast, standard cameras provide intensity measurements
(frames) that do not depend on motion direction. Our method extracts features
on frames and subsequently tracks them asynchronously using events, thereby
exploiting the best of both types of data: the frames provide a photometric
representation that does not depend on motion direction and the events provide
low-latency updates. In contrast to previous works, which are based on
heuristics, this is the first principled method that uses raw intensity
measurements directly, based on a generative event model within a
maximum-likelihood framework. As a result, our method produces feature tracks
that are both more accurate (subpixel accuracy) and longer than the state of
the art, across a wide variety of scenes.Comment: 22 pages, 15 figures, Video: https://youtu.be/A7UfeUnG6c
Experimental and simulation study results for video landmark acquisition and tracking technology
A synopsis of related Earth observation technology is provided and includes surface-feature tracking, generic feature classification and landmark identification, and navigation by multicolor correlation. With the advent of the Space Shuttle era, the NASA role takes on new significance in that one can now conceive of dedicated Earth resources missions. Space Shuttle also provides a unique test bed for evaluating advanced sensor technology like that described in this report. As a result of this type of rationale, the FILE OSTA-1 Shuttle experiment, which grew out of the Video Landmark Acquisition and Tracking (VILAT) activity, was developed and is described in this report along with the relevant tradeoffs. In addition, a synopsis of FILE computer simulation activity is included. This synopsis relates to future required capabilities such as landmark registration, reacquisition, and tracking
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