Although the development of high dynamic range images allowed broadening sky vault analysis for lighting research purposes, the sky classification is still performed using subjective methods. There is no established metric for comparison, only isolated approaches that do not address the characteristics of the images. This paper presents a proposed multidimensional method for analysing light from sky vault high dynamic range images. A Matlab routine was applied. It uses a classification system to check the relevance of each image attribute, choosing the most suitable sky model from the ISO 15469:2004 (CIE S 011/E:2003) standard. The results, that can be plotted or exported, indicate that the routine is able to choose the most relevant model for the photographed sky, thus allowing the creation of a sky classification database.
Get full access to this article
View all access options for this article.
References
1.
Stumpfel J, Tchou C, Jones A, Hawkins T, Wenger A, Debevec P. Direct HDR capture of the sun and sky: Proceedings of the 3rd International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa, 3–5 November 2004, New York: ACM, pp.145–149.
2.
ReinhardEWardGPattanaikSDebevecP. High Dynamic Range Imaging: Acquisition, Display, and Image-based Lighting, San Francisco: Elsevier; Morgan Kaufmann Publisher, 2006.
3.
InaniciMNavvabM. The Virtual Lighting Laboratory: Per-pixel luminance data analysis. Leukos2006; 3: 89–104.
4.
Cheney K, Inanici M. Image based rendering: Using high dynamic range photographs to light architectural scenes: Proceedings of the ACSA West Central Fall Conference Architecture in the Age of Digital Reproduction, University of Illinois Champaigne-Urbana, 23–26 October 2008, pp.1–7.
5.
InaniciM. Evalution of high dynamic range image-based sky models in lighting simulation. Leukos2010; 7: 69–84.
6.
Scarazzato PS, Souza DF. HDR images in the study of sky vault luminance distribution and identification of prevailing sky types: Proceedings of the 27th Session of the CIE, Sun City, South Africa: Illuminating Engineering Society of South Africa, 9–16 July 2011, pp.758–766.
7.
Commission Internationale de l’Eclairage. CIE S 011/E:2003 Spatial Distribution of Daylight – CIE Standard General Sky. Vienna: CIE, 2003.
8.
Henderson-SellersAHughesNWilsonM. Cloud cover archiving on a global scale: A discussion of principles. Bulletin of the American Meteorological Society1981; 62: 1300–1307.
9.
CazorlaAOlmoFAlados-ArboledasL. Development of a sky imager for cloud cover assessment. Journal of the Optical Society of America A2008; 25: 29–39.
10.
Secretariat of the World Meteorological Organization. Meteorological Service for International Air Navigation – Volume II, Geneva: World Meteorological Organization, 2004.
11.
Office of the Federal Coordinator for Meteorological Services and Supporting Research. Federal Meteorological Handbook No. 1: Surface Weather Observations and Reports, Washington, DC: US Department of Commerce/ National Oceanic and Atmospheric Administration, 2005.
12.
UQAM-Montréal CM. Aviation Routine Weather Report (METAR). Centre Météo UQAM-Montréal, 2014.
13.
Kittler R, Darula S, Perez R. A set of standard skies characterising daylight conditions for computer and energy conscious design. Final Report of the American Grant Project US-SK 1998; 92052.
14.
Souza DF, Scarazzato PS, Pedrini H. Sky classification metrics for high dynamic range images: Proceedings of Towards a New Century of Light – CIE Centenary Conference. Paris. Vienna: CIE, 2013, pp.817–825.
15.
Souza DF, Scarazzato PS, Pedrini H. Influência da divisão do céu em cálculos de disponibilidade de luz a partir de imagens de grande alcance dinâmico. In: Anais do XII Encontro Nacional e VIII Encontro Latino Americano de Conforto no Ambiente Construído. 2013, pp. 1–10.
16.
ShahriarA. Algorithm for determining CIE Standard General Sky occurrence from digital sky images. Architectural Science Review2013; 56: 266–271.
17.
Lighting Research Group. Building Technologies Department, Environmental Energy Techonologies Division, Lawrence Berkeley National Laboratory, Berkeley Hills, CA, USA.
18.
InaniciMN. Evaluation of high dynamic range photography as a luminance data acquisition system. Lighting Research and Technology2006; 38: 123–134.
19.
JacobsA. High dynamic range imaging and its application in building research. Advances in Building Energy Research2007; 1: 177–202.
20.
Souza DF, Scarazzato PS. Estudos e avaliações pós-ocupação da iluminação no espaço construído através de imagens HDR e câmeras digitais compactas. In: Do Moderno ao Digital: Desafios de uma transição. São Paulo, 2009, pp.443–445.
BanterleFArtusiADebattistaKChalmersA. Advanced High Dynamic Range Imaging: Theory and Practice, Natick, MA: AK Peters/CRC Press, 2011.
23.
Van Den HeuvelFAVerwaalRBeersB. Calibration of fisheye camera systems and the reduction of chromatic aberration: Proceedings ISPRS Commission V Symposium, IntArch-PhRS. Citeseer, 2006, pp.1–6.
24.
KittlerRKocifajMDarulaS. Daylight Science and Daylighting Technology, New York: Springer-Verlag, 2011.
25.
Blanco-MurielMAlarcón-PadillaDCLópez-MoratallaTLara-CoiraM. Computing the solar vector. Solar Energy2001; 70: 431–441.
26.
SnyderJP. Map Projections: A Working Manual, Washington, DC: US Geological Survey, 1987.
27.
RoyGHaymanSJulianW. Sky analysis from CCD images: Cloud cover. Lighting Research and Technology2001; 33: 211–221.
KobavMBBizjakGDumortierD. Characterization of the sky scanner measurements based on CIE and ISO standard CIE S 011/2003. Lighting Research and Technology2013; 45: 504–512.
30.
TregenzaPWatersI. Daylight coefficients. Lighting Research and Technology1983; 15: 65–71.
31.
Kobav MB. Development and validation of methods used to compute time values of indoor daylight illuminances. PhD Thesis, Faculty of Electrical Engineering University of Ljublana, Slovenia, 2009.
32.
Ward G. Photosphere 1.8.7u. Retrieved 15 March 2012, from http://anyhere.com/.
