Spectral Sensitivity Estimation Without a Camera

• URL: http://arxiv.org/abs/2304.11549v2
• Date: Tue, 11 Jul 2023 13:34:50 GMT
• Title: Spectral Sensitivity Estimation Without a Camera
• Authors: Grigory Solomatov and Derya Akkaynak
• Abstract summary: A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. We propose a framework for spectral sensitivity estimation that does not require any hardware, but also does not require physical access to the camera itself. We provide our code and predicted sensitivities for 1,000+ cameras, and discuss which tasks can become trivial when camera responses are available.
• Score: 6.599344783327053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession. To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware, but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space. Different than other work, we use publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide our code and predicted sensitivities for 1,000+ cameras, and discuss which tasks can become trivial when camera responses are available.

Related papers

• PeLiCal: Targetless Extrinsic Calibration via Penetrating Lines for RGB-D Cameras with Limited Co-visibility [11.048526314073886]
  We present PeLiCal, a novel line-based calibration approach for RGB-D camera systems exhibiting limited overlap. Our method leverages long line features from surroundings, and filters out outliers with a novel convergence voting algorithm.
  arXiv  Detail & Related papers  (2024-04-22T07:50:24Z)
• Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model [83.9497193551511]
  We introduce Lighting Every Darkness (LED), which is effective regardless of the digital gain or the camera sensor. LED eliminates the need for explicit noise model calibration, instead utilizing an implicit fine-tuning process that allows quick deployment and requires minimal data. LED also allows researchers to focus more on deep learning advancements while still utilizing sensor engineering benefits.
  arXiv  Detail & Related papers  (2023-08-07T10:09:11Z)
• Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras [9.801893730708134]
  In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. For accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images
  arXiv  Detail & Related papers  (2022-09-22T22:20:30Z)
• Extrinsic Camera Calibration with Semantic Segmentation [60.330549990863624]
  We present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle. We evaluate our method on simulated and real-world data to demonstrate low error measurements in the calibration results.
  arXiv  Detail & Related papers  (2022-08-08T07:25:03Z)
• Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [66.4525391417921]
  We design and evaluate a multi-sensor drone detection system. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
  arXiv  Detail & Related papers  (2022-07-05T10:00:58Z)
• Lasers to Events: Automatic Extrinsic Calibration of Lidars and Event Cameras [67.84498757689776]
  This paper presents the first direct calibration method between event cameras and lidars. It removes dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements.
  arXiv  Detail & Related papers  (2022-07-03T11:05:45Z)
• Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles [10.490646039938252]
  We propose a generic and task-oriented self-health-maintenance framework for cameras based on data- and physically-grounded models. We implement the framework on a real-world ground vehicle and demonstrate how a camera can adjust its parameters to counter a poor condition. Our framework not only provides a practical ready-to-use solution to monitor and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems.
  arXiv  Detail & Related papers  (2021-12-10T11:14:44Z)
• Infrastructure-based Multi-Camera Calibration using Radial Projections [117.22654577367246]
  Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. Infrastucture-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. We propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach.
  arXiv  Detail & Related papers  (2020-07-30T09:21:04Z)
• Learning Camera Miscalibration Detection [83.38916296044394]
  This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
  arXiv  Detail & Related papers  (2020-05-24T10:32:49Z)
• A Single RGB Camera Based Gait Analysis with a Mobile Tele-Robot for Healthcare [9.992387025633805]
  This work focuses on the analysis of gait, which is widely adopted for joint correction and assessing any lower limb or spinal problem. On the hardware side, we design a novel marker-less gait analysis device using a low-cost RGB camera mounted on a mobile tele-robot.
  arXiv  Detail & Related papers  (2020-02-11T21:42:22Z)

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.