Using the Standard Deviation of a Region of Interest in an Image to Estimate Camera to Emitter Distance

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

In this study, a camera to infrared diode (IRED) distance estimation problem was analyzed. The main objective was to define an alternative to measures depth only using the information extracted from pixel grey levels of the IRED image to estimate the distance between the camera and the IRED. In this paper, the standard deviation of the pixel grey level in the region of interest containing the IRED image is proposed as an empirical parameter to define a model for estimating camera to emitter distance. This model includes the camera exposure time, IRED radiant intensity and the distance between the camera and the IRED. An expression for the standard deviation model related to these magnitudes was also derived and calibrated using different images taken under different conditions. From this analysis, we determined the optimum parameters to ensure the best accuracy provided by this alternative. Once the model calibration had been carried out, a differential method to estimate the distance between the camera and the IRED was defined and applied, considering that the camera was aligned with the IRED. The results indicate that this method represents a useful alternative for determining the depth information.

Related collections

Most cited references 24

Record: found
Abstract: found
Article: not found

Modeling the radiation pattern of LEDs.

Ivan Moreno, Ching-Cherng Sun (2008)

Light-emitting diodes (LEDs) come in many varieties and with a wide range of radiation patterns. We propose a general, simple but accurate analytic representation for the radiation pattern of the light emitted from an LED. To accurately render both the angular intensity distribution and the irradiance spatial pattern, a simple phenomenological model takes into account the emitting surfaces (chip, chip array, or phosphor surface), and the light redirected by both the reflecting cup and the encapsulating lens. Mathematically, the pattern is described as the sum of a maximum of two or three Gaussian or cosine-power functions. The resulting equation is widely applicable for any kind of LED of practical interest. We accurately model a wide variety of radiation patterns from several world-class manufacturers.

0 comments Cited 33 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Modeling the space of camera response functions.

Michael D Grossberg, Shree K Nayar (2004)

Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image intensity of an imaging system is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database, we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter empirical model of response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at http://www.cs.columbia.edu/CAVE.

0 comments Cited 29 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Radiometric Self Calibration

T. Mitsunaga, S.K. Nayar (2025)

0 comments Cited 4 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Title: Sensors (Basel, Switzerland)

Publisher: Molecular Diversity Preservation International (MDPI)

ISSN (Electronic): 1424-8220

Publication date Collection: 2012

Publication date (Electronic): 03 May 2012

Volume: 12

Issue: 5

Pages: 5687-5704

Affiliations

[1 ] Telecommunication Department, University of Oriente, Av. de las Américas, SN, Santiago de Cuba 90100, Cuba; E-Mails: acano@ 123456fie.uo.edu.cu (A.E.C.-G.); ainfante@ 123456fie.uo.edu.cu (A.I.); ypompa@ 123456fie.uo.edu.cu (Y.P.-C.)

[2 ] Electronics Department, University of Alcalá, Superior Polytechnic School, University Campus, Alcalá de Henares 28871, Madrid, Spain; E-Mails: pedro.fernandez@ 123456depeca.uah.es (P.F.); felipe@ 123456depeca.uah.es (F.E.)

Author notes

[* ]Author to whom correspondence should be addressed; E-Mail: lazaro@ 123456depeca.uah.es ; Tel.: +34-918-856-540; Fax: +34-918-856-591.

Article

Publisher ID: sensors-12-05687

DOI: 10.3390/s120505687

PMC ID: 3386707

PubMed ID: 22778608

SO-VID: dbb787c1-c672-458c-afc9-71ce6d8fc91f

License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

History

Date received : 15 March 2012

Date revision received : 09 April 2012

Date accepted : 13 April 2012

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.