Automatic Color Reference Target Detection

The use of standard color reference targets at image acquisition allows to compensate for different camera characteristics, illumination conditions and exposure times, ensuring true colors in digital photo workflows. Reliable automatic detection of reference targets makes color correction faster, and this becomes critical in mass digitization processes. The existing automatic algorithms usually assume that there is little perspective distortion and/or that the scanning resolution is known, achieving very limited results for example when the relative size of the color target is unknown. In this paper we present a preprocessing step that aims at automatically detecting a region of interest (ROI) where the reference target is located. We compare the performance of one of the available automatic tools (CCFind) with and without this preprocessing step, and show a considerable improvement in the detection of color reference targets in a new challenging dataset. In addition, a simple template matching approach is compared with the performance of CCFind. The results show that the selection of a smaller ROI complements well with the existing approaches and helps to improve detection. Introduction The aim of color management is to get colors to match from input to output. When a camera captures true colors, we would like our monitor or printer to display these colors accurately. This becomes a challenge with the huge amount of devices existing nowadays, since each input device interprets colors differently, and the way they are interpreted also varies with the manufacturer. In fact, even two camera models from the same manufacturer can capture colors differently. Light also comes in many different ways and colors. Depending on the time of the day, natural light can go from bluish to yellowish, and this variation is even more important for artificial indoor lighting. Our human visual system compensates for all of these different color light sources without us really noticing. However, since cameras see light literally, these subtle shifts can pose a problem. In order to reduce the differences between the images acquired by different devices and under different illumination conditions, standard color reference targets are used. Capturing images including these standard references thus make color corrections faster, since we can for instance apply the same white balance to a group of images acquired under the same lighting conditions. Ensuring true colors is critical in any photo workflow because it provides a reference point for posterior editing. In areas such as mass digitization environments, tools for automated quality analysis are a prerequisite for the management of technical content quality [1]. Existing systems are mostly based on reference based measurement methods, and some of the automated quality assessment (QA) algorithms proposed in the literature rely on the presence of reference targets within every digitized image file. Since the standard reference and source media could be located at any position within the digitized image, extraction of reference targets is needed prior to analysis and color correction. This paper proposes an algorithm that aims at automatically finding a region of interest (ROI) where a standard color reference target is located, facilitating the work of currently existing automatic techniques that detect reference targets. The performance of these techniques, with and without the ROI selection step, has been evaluated. The remainder of this paper is organized as follows: Color Reference Target Detection presents several popular color reference targets and the existing approaches to automatically detect them within an image. Proposed System Overview focuses on the description of the proposed system, where the automatic ROI selection step is described. In addition, two automatic reference target finders are presented. The experimental results are described in Experimental Results, where we justify the selection of some parameters and we compare the performance of the proposed system with the current approaches (without any preprocessing step). Finally, Conclusions and Future Work are discussed. Color Reference Target Detection The employment of reference targets for professional digitization or photography is common nowadays. Color reference targets are therefore of central importance in any mass digitization process. Depending on their type, reference targets allow a precise quantification of many different parameters influencing the digitization. Knowing the real size and the colorimetry of the reference target, they allow to determine the scale, rotation and any distortions present in the digitized asset, as well as the color and illumination deviation and uniformity. In the following, some of the most popular color reference targets in use today are presented: • The ColorChecker Classic target [2] (Figure 1(a)), initially developed by McCamy et al. [3] in 1976 and commercialized as the ”Macbeth ColorChecker”. It contains 24 natural, chromatic, primary and gray scale uniformly-sized squares in a wide range of colors printed on a 8,25” × 11” cardboard. It has become the industry standard, since it is still the most common tool employed for color comparison. • The ColorChecker Digital SG [2] (Figure 1(b)) is an enhancement of the ColorChecker Classic for digital photography. It contains an extended color palette in the form of 140 quadratic patches chosen for its location in color space to expand the color gamut, and it is printed on a 8,25” × 11” cardboard as well. • The Universal Test Target (UTT) [4] (Figure 1(c)) is a recent open standard test target designed to provide insight into the 119 22nd Color and Imaging Conference Final Program and Proceedings and 2nd Congress of the International Academy of Digital Pathology