Removal of noise and radial lens distortion during calibration of computer vision systems

Wang, Zhenzhou

doi:10.1364/oe.23.011341

Cited by 24 publications

(7 citation statements)

References 25 publications

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“…Typically, determination of these geometric descriptions, or calibration, is crucial for measurement accuracy. Although benefitting from advanced calibration methods [4][5][6][7], the measurement accuracy can be better than 1/10000 of the length of the measuring field. However, such high accuracy can only be achieved if the geometry of optical components is stable over the time between calibration and measurement.…”

Section: Introductionmentioning

confidence: 99%

Pre-calibration-free 3D shape measurement method based on fringe projection

Zhong¹,

Li²,

Li³

et al. 2016

Opt. Express

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This paper presents a pre-calibration-free 3D shape measurement method based on fringe projection. Unlike ordinary methods, it performs calibration and 3D shape measurement concurrently. The captured phase-coded fringe images are utilized to obtain homogenous control points from two camera viewpoints, and the rough 3D structure of these points can be retrieved. Further, a constrained non-linear least-squares optimization model is established to determine the in situ geometry of the optical components, and then, the 3D scene is reconstructed. This method provides an accurate 3D shape measurement capability even during disturbance of the optical geometry. Moreover, not requiring a preliminary calibration process makes the system ultra-flexible. The performance of this method was verified by experiments.

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Section: Introductionmentioning

confidence: 99%

Pre-calibration-free 3D shape measurement method based on fringe projection

Zhong¹,

Li²,

Li³

et al. 2016

Opt. Express

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“…The most common distortion model of the camera is the radial distortion [58,59]. Such camera distortion can be expressed as:…”

Section: Proposed Self-calibration Methodsmentioning

confidence: 99%

Efficient camera self-calibration method for remote sensing photogrammetry

Liu

2018

Opt. Express

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Internal parameter calibration of remote sensing cameras (RSCs) is a necessary step in remote sensing photogrammetry. On-orbit camera calibration widely adopts external ground control points (GCPs) to measure its internal parameters. However, accessible and available GCPs are not easy to achieve when cameras work on a satellite platform. In this paper, we propose an efficient camera self-calibration method using a micro-transceiver in conjunction with deep learning. A supervised learning set is produced by the micro-transceiver, where multiple two-dimensional diffraction grids are produced and transformed into multiple auto-collimating sub-beams equivalent to infinite target-point training examples. A deep learning network is used to invert the learnable internal parameters. The micro-transceiver can be easily integrated into the internal structure of RSCs allowing to calibrate them independently on external ground control targets.

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“…In fact, only some special functions are useful. Thus, functional selection was adopted to remove redundant, noisy functions [24]. In addition, feature weighting is a generalization technology, which assigns a weight to each feature within the range of [0, 1] rather than delete that feature [25].…”

Section: Cluster Analysismentioning

confidence: 99%

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Xie¹,

Zhang²,

Liu³

2021

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Artificial intelligence and fifth generation (5G) technology are widely adopted to evaluate the classroom poses of college students, with the help of campus video surveillance equipment. To ensure the effective learning in class, it is important to detect and intervene in abnormal behaviors like sleeping and using cellphones in time. Based on spatiotemporal representation learning, this paper presents a deep learning algorithm to evaluate classroom poses of college students. Firstly, feature engineering was adopted to mine the moving trajectories of college students, which were used to determine student distribution and establish a classroom prewarning system. Then, k-means clustering (KMC) was employed for cluster analysis on different student groups, and identify the features of each group. For a specific student group, the classroom surveillance video was decomposed into several frames; the edge of each frame was extracted by edge detection algorithm, and imported to the proposed convolutional neural network (CNN). Experimental results show that our algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.

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Removal of noise and radial lens distortion during calibration of computer vision systems

Cited by 24 publications

References 25 publications

Pre-calibration-free 3D shape measurement method based on fringe projection

Pre-calibration-free 3D shape measurement method based on fringe projection

Efficient camera self-calibration method for remote sensing photogrammetry

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

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