Four-point-algorithm for the recovery of the pose of a one-dimensional camera with unknown focal length

Penne, Rudi; Veraart, Jelle; Abbeloos, Wim; Mertens, Luc

doi:10.1049/iet-cvi.2010.0192

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…With the increase of nimble mobile devices, a huge amount of digital images are taken using hand-held cameras every day [1][2][3][4][5]. However, camera shake is often inevitable, resulting in undesirable motion blurring.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality evaluation for motion deblurring

Qian

2018

IET Computer Vision

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Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously. Unfortunately, most of the existing quality metrics are not designed for multiply distorted images, so they are limited in predicting the quality of motion deblurred images. In this study, the authors propose a new quality metric for motion deblurred images by measuring NRRB. For a motion deblurred image, the noise level is first estimated. Then the ringing effect is measured by incorporating visual saliency model to adapt to the characteristic of the human visual system. A reblurring-based method is proposed to extract similarity features between a motion deblurred image and its re-blurred version for evaluating the residual blur. Finally, the overall quality score of a motion deblurred image is obtained by pooling the scores of noise, ringing and blur. Experimental results conducted on a motion deblurring database demonstrate that the proposed metric significantly outperforms the existing quality metrics. In addition, the proposed NRRB metric is used for improving the existing generalpurpose no-reference metrics, and very encouraging results are achieved.

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Section: Introductionmentioning

confidence: 99%

Perceptual quality evaluation for motion deblurring

Qian

2018

IET Computer Vision

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“…Camera pose estimation is widely used in daily and industrial applications, such as computer vision(CV) [1], visual servo, human computer interaction [2,3], robotics [4],and aerospace industry [5] etc, particularly, it has received much attention in both the Photogram entry and CV, even new techniques have been emerging endlessly.…”

Section: Introductionmentioning

confidence: 99%

An Improved Orthogonal Iterative Algorithm for Monocular Camera Pose Estimation

Shi¹,

Dong²,

Zheng³

2017

dtcse

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Abstract. In this paper, we focus on the classical perspective-n-point(PnP) problem in monocular camera pose estimation and propose an improved orthogonal iterative algorithm. The key idea is to integrate the steps in each iteration, and extract the repetitive calculation of each iteration. The repetitive computation in each iteration can be abstracted and done before iteration process. The computational complexity of each iteration is reduced from to ( ) O n to (1) O . So that, more iterations can be done in a short time and the accuracy is improved as well. The simulation results and real experiments results show the efficiency and accuracy of our entry accepted iterative algorithms, our algorithm can maintain high pose estimation accuracy. This algorithm is more likely to converge to correct pose in a short run time, which makes it more applicable for real time applications.

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FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs

Zhang

Yang

et al. 2022

Computer Methods and Programs in Biomedicine

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Four-point-algorithm for the recovery of the pose of a one-dimensional camera with unknown focal length

Cited by 4 publications

References 16 publications

Perceptual quality evaluation for motion deblurring

Perceptual quality evaluation for motion deblurring

An Improved Orthogonal Iterative Algorithm for Monocular Camera Pose Estimation

FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs

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