An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections

• URL: http://arxiv.org/abs/2302.14409v1
• Date: Tue, 28 Feb 2023 08:44:00 GMT
• Title: An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections
• Authors: Andrea Montibeller and Fernando P\'erez-Gonz\'alez
• Abstract summary: In-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution. Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load. We propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens.
• Score: 77.34726150561087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radial correction distortion, applied by in-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution. Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load; however, with ever more prevalent complex distortion corrections their performance is unsatisfactory. In this paper we propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens. We also introduce a statistic called cumulative peak of correlation energy (CPCE) that allows for an efficient early stopping strategy. Experiments on a large dataset of in-camera and out-camera radially corrected images show that our solution improves the state of the art in terms of both accuracy and computational cost.

Related papers

• Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation? [39.593903427245664]
  This paper compares radial distortion solvers with a simple-to-implement approach that combines an efficient pinhole solver with sampled radial distortion parameters. Experiments on multiple datasets and RANSAC variants show that this simple approach performs similarly or better than the most accurate minimal distortion solvers.
  arXiv  Detail & Related papers  (2024-10-08T12:30:29Z)
• VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
  We introduce a novel methodology that extends Pose Graph Optimization techniques. We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
  arXiv  Detail & Related papers  (2024-03-25T17:47:03Z)
• Cameras as Rays: Pose Estimation via Ray Diffusion [54.098613859015856]
  Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views. We propose a distributed representation of camera pose that treats a camera as a bundle of rays. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D.
  arXiv  Detail & Related papers  (2024-02-22T18:59:56Z)
• How to turn your camera into a perfect pinhole model [0.38233569758620056]
  We propose a novel approach that involves a pre-processing step to remove distortions from images. Our method does not need to assume any distortion model and can be applied to severely warped images. This model allows for a serious upgrade of many algorithms and applications.
  arXiv  Detail & Related papers  (2023-09-20T13:54:29Z)
• Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction [54.00007868515432]
  Existing methods face challenges in estimating the accurate correction field due to the uniform velocity assumption. We propose a geometry-based Quadratic Rolling Shutter (QRS) motion solver, which precisely estimates the high-order correction field of individual pixels. Our method surpasses the state-of-the-art by +4.98, +0.77, and +4.33 of PSNR on Carla-RS, Fastec-RS, and BS-RSC datasets, respectively.
  arXiv  Detail & Related papers  (2023-03-31T15:09:18Z)
• Self-Supervised Camera Self-Calibration from Video [34.35533943247917]
  We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods.
  arXiv  Detail & Related papers  (2021-12-06T19:42:05Z)
• Self-Calibrating Neural Radiance Fields [68.64327335620708]
  We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions.
  arXiv  Detail & Related papers  (2021-08-31T13:34:28Z)
• Efficient Real-Time Radial Distortion Correction for UAVs [1.7149364927872015]
  We present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU) This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. We propose a fast and robust minimal solver for simultaneously estimating the focal length, radial distortion profile and motion parameters from homographies.
  arXiv  Detail & Related papers  (2020-10-08T18:34:56Z)
• A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
  We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
  arXiv  Detail & Related papers  (2020-07-21T10:03:42Z)
• UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models [8.484676769284578]
  We propose a generic scale-aware self-supervised pipeline for estimating depth, euclidean distance, and visual odometry from unrectified monocular videos. The proposed algorithm is evaluated further on the KITTI rectified dataset, and we achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2020-07-13T20:35:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.