Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration - PubMed (original) (raw)

Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration

Derya Akkaynak et al. J Opt Soc Am A Opt Image Sci Vis. 2014.

Abstract

Commercial off-the-shelf digital cameras are inexpensive and easy-to-use instruments that can be used for quantitative scientific data acquisition if images are captured in raw format and processed so that they maintain a linear relationship with scene radiance. Here we describe the image-processing steps required for consistent data acquisition with color cameras. In addition, we present a method for scene-specific color calibration that increases the accuracy of color capture when a scene contains colors that are not well represented in the gamut of a standard color-calibration target. We demonstrate applications of the proposed methodology in the fields of biomedical engineering, artwork photography, perception science, marine biology, and underwater imaging.

PubMed Disclaimer

Figures

Fig. 1

Fig. 1

Basic image-processing pipeline in a consumer camera.

Fig. 2

Fig. 2

(a) An uncompressed image. (b) Artifacts after jpg compression: (1) grid-like pattern along block boundaries, (2) blurring due to quantization, (3) color artifacts, (4) jagged object boundaries. Photo credit: Dr. Hany Farid. Used with permission. See [3] for full resolution images.

Fig. 3

Fig. 3

Workflow proposed for processing raw images. Consumer cameras can be used for scientific data acquisition if images are captured in raw format and processed manually so that they maintain a linear relationship to scene radiance.

Fig. 4

Fig. 4

Human color-matching functions for the CIE XYZ color space for 2° observer and spectral sensitivities of two cameras; Canon EOS 1Ds mk II and Nikon D70.

Fig. 5

Fig. 5

(a) Irradiance of daylight at noon (CIE D65 illuminant) and noon daylight on a sunny day recorded at 3 m depth in the Aegean Sea. (b) Reflectance spectra of blue, orange, and red patches from a Macbeth ColorChecker (MCC). Reflectance is the ratio of reflected light to incoming light at each wavelength, and it is a physical property of a surface, unaffected by the ambient light field, unlike radiance. (c) Radiance of the same patches under noon daylight on land and (d) underwater.

Fig. 6

Fig. 6

(a) An original scene. Inset at lower left: Bayer mosaic. (b) Close-ups of marked areas after high-quality (adaptive) and (c) low-quality (non-adaptive) demosaicing. Artifacts shown here are zippering on the sides of the ear and false colors near the white pixels of the whiskers and the eye.

Fig. 7

Fig. 7

(a) Examples of photographic calibration targets. Top to bottom: Sekonik Exposure Profile Target II, Digital Kolor Kard, Macbeth ColorChecker (MCC) Digital. (b) Reflectance spectra (400–700 nm) of Spectralon targets (black curves, prefixed with SRS-), gray patches of the MCC (purple), and a white sheet of printer paper (blue). Note that MCC 23 has a flatter spectrum than the white patch (MCC 19). The printer paper is bright and reflects most of the light, but it does not do so uniformly at each wavelength.

Fig. 8

Fig. 8

Chromaticity of MCC patches captured by two cameras, whose sensitivities are given in Fig. 4, in device-dependent and independent color spaces.

Fig. 9

Fig. 9

Using more patches for a color transformation does not guarantee increased transformation accuracy. In this example, color-transformation error is computed after 1–24 patches are used. There were many possible ways the patches could have been selected; only three are shown here. Regardless of patch ordering, overall color-transformation error is minimized after the inclusion of the 18th patch. The first six patches of orders 1 and 2 are chromatic, and for order 3, they are achromatic. The errors associated with order 3 are higher initially because the scene, which consists of a photo of an MCC, is mostly chromatic. Note that it is not possible to have the total error be identically zero even in this simple example due to numerical error and noise.

Fig. 10

Fig. 10

Features from three different dive sites that could be used for SSCC. This image first appeared in the December 2012 issue of Sea Technology magazine.

Fig. 11

Fig. 11

Scene-specific color transformation improves accuracy. (a) A “non-ordinary” scene that has no chromaticity overlap with the patches in the calibration target. (b) Mean error after SSCC is significantly less than after using a calibration chart. (c) An “ordinary” scene in which MCC patches span the chromaticities in the scene. (d) Resulting error between the MCC and scene-specific color transformation is comparable, but on average, still less for SSCC.

Fig. 12

Fig. 12

Temperature distribution along the microchip channel, which is locally heated to 39°C (colored region) while the rest was kept below 32°C (black region).

Fig. 13

Fig. 13

Example II: Use of inexpensive COTS cameras for accurate artwork photography. (a) Oil painting under daylight illumination. (b) Thirty-six points from which ground-truth spectra were measured. (b) Chromatic loci of the ground truth samples compared to MCC patches under identical illumination. (d) sRGB representation of the colors used for scene-specific calibration. Artwork: Fulya Akkaynak.

Fig. 14

Fig. 14

Example III Capturing photographs under monochromatic low-pressure sodium light. (a) A pair of fabrics under broadband light. (b) A jpg image taken with the auto settings of a camera, under monochromatic sodium light. (c) Image processed using SSCC according to the flow in Fig. 3.

Fig. 15

Fig. 15

Example IV: In situ capture of (a) an underwater habitat and (b) a camouflaged cuttlefish (marked with white arrow) using SSCC with features similar to those shown in Fig. 10 for Urla, Turkey. (c) and (d) are jpg outputs directly from the camera operated in auto mode and have a visible red tint as a consequence of in-camera processing.

Fig. 16

Fig. 16

Example V: Consistent underwater color correction. (a) In each frame, the color chart on the left was used for calibration, and the one on the right was for testing. Images were taken in Toyota Reef, Fiji. (b) Average error for several color-corrected methods for training and testing. Our method achieves the lowest error and is the only method to improve over the raw images of the test chart.

References

    1. Chakrabarti A, Scharstein D, Zickler T. An empirical camera model for internet color vision. Proceedings of British Machine Vision Conference; 2009. p. 51.1.
    1. Seon Joo K, Hai Ting L, Zheng L, Süsstrunk S, Lin S, Brown MS. A new in-camera imaging model for color computer vision and its application. IEEE Trans Pattern Anal Mach Intell. 2012;34:2289–2302. -PubMed
    1. Farid H. That looks fake! 2011 retrieved http://www.fourandsix.com/blog/2011/6/29/that/looks/fake.html.
    1. Levin N, Ben-Dor E, Singer A. A digital camera as a tool to measure colour indices and related properties of sandy soils in semi-arid environments. Int J Remote Sens. 2005;26:5475–5492.
    1. De La Barrera E, Smith WK. Perspectives in Biophysical Plant Ecophysiology: A Tribute to Park S. Nobel. Unam; 2009.

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources