Optic flaws detection and location based on a plenoptic camera

Luan, Yinsen; Xu, Bing; Tang, Gong‐Jian

doi:10.3788/col201715.041102

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The localization techniques based on vision sensors can be divided into two categories. One involves collecting images with a mobile camera and the location of the camera is determined, while the other collects images with a fixed camera and the target location in the image is determined [9]. Large graphic computation is required in vision localization systems, because of the hardware limitations, and although the systems have a high precision in a specific environment, the real-time performance may not be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Wang

Liu

et al. 2018

Sensors

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This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

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Section: Introductionmentioning

confidence: 99%

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Wang

Liu

et al. 2018

Sensors

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Development of a light field measuring system with corrected distortion based on imaging analysis

Zhou

Wang

2020

Appl. Opt.

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A light field microscope can realize three-dimensional measurement through only one exposure by the addition of a critical optical component called a microlens array (MLA). MLA errors will introduce aberrations in the captured image and then affect measurement results. This paper proposes a light field measuring system with aberration correction based on imaging analysis. The effect of MLA errors is investigated and quantitatively analyzed through a series of simulation studies. Aberration correction is realized based on computational imaging, in which an image segmentation and fusion distortion model is employed to correct the distortion, while a gradient-based linear recognition algorithm is used to address MLA rotation errors. Experimental results show that the developed light field measuring system can achieve improved measurement accuracy by correcting aberrations with the proposed algorithms.

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Optic flaws detection and location based on a plenoptic camera

Cited by 2 publications

References 12 publications

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Development of a light field measuring system with corrected distortion based on imaging analysis

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