Light-field cameras are gaining attention for their unique light gathering and post-capture processing capabilities. In our previous work, we combined light-field imaging with structured light technology to realize three-dimensional (3D) reconstruction for highly reflective surface. However, due to the lack of effective calibration of the light-field camera and reconstruction systems, it cannot meet the requirements of precise 3D measurement. A universal sub-aperture image extraction algorithm for the light-field camera with hexagonal microlens is presented to accurately extract the multidirectional images. Then, we explore the spatial relationship in equivalent camera array (ECA) of focused light-field camera according to the perspective variation of sub-aperture images. After that, an accurate 3D measurement system based on ECA model is proposed to achieve 3D measurement for highly reflective surface. To test the system’s practical performance, a precision analysis is conducted for a standard gauge block with ground truth. By comparing the reconstruction result of traditional method and ours, we demonstrated the validity of the proposed method to perform 3D measurement for highly reflective surface.
The angle-based intersection measurement system is a high-precision overall measurement network based on the perspective observation in space. However, when the target is moving, the angular intersection failure will cause dynamic error to limit its application in the field of dynamic measurement. Aiming at this problem, Firstly, this paper analyze the sources of dynamic error from the principle, studies the influencing factors of system dynamic error include :the motion state of the measured object, station deployment. Secondly, constructs a mathematical model to predict the dynamic error of the measurement system. Finally, design some simulation experiments to quantify the measurement dynamic error at different measure conditions. The results show that under the measurement conditions of the measurement area is 10m x 10m x 1m, observation angle uncertainty is 2′′, the measured target moves at 0.05m/s, the average dynamic error of the measured area under the 0_4 deployment is the minimum, and value is 0.25mm.
This paper proposes a hierarchical feature-matching model for the typical faults detection, which is a big challenge in the trouble of a moving freight car detection system (TFDS) due to the constant color and complex background of images. The proposed model divides fault detection into two stages: image segmentation and parallel shape matching. In the process of segmentation, a fast adaptive Markov random field (FAMRF) algorithm is presented based on the image pyramid model and affinity propagation theory. In the process of shape matching, a shape descriptor named exact height function (EHF) is introduced on the basis of parallel dynamic programming. The experimental results indicate that the proposed hierarchical model combined with FAMRF and EHF can achieve automatic detection of an air brake system, bogie block key, and fastening bolt. The proposed model achieves high detection accuracy and great robustness, and it can be effectively applied to the fault detection in TFDS.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.