PO43
COLOUR CONTRIBUTION IN SPONTANEOUS
IDENTIFICATION OF COPPER MATERIALS
Hiroki Ishiyama et al.
DOI 10.25039/x47.2020.PO43

Paper accepted for the 5th CIE Symposium on Colour andVisual Appearance

The paper was selected by the International Scientific Committee (ISC) for presentation at the 5th CIE
Symposium on Colour and Visual Appearance, Hong Kong, CN, April 21–22, 2020, which, due to thecorona pandemic, could not take place. The paper has not been peer-reviewed by CIE.

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Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALSPO43

COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF
COPPER MATERIALS
1Ishiyama, H. 1 , Tanaka, M. 2 , Horiuchi, T. 1
Graduate School of Science and Engineering, Chiba University, JAPAN,
2
College of Liberal Arts and Sciences, Chiba University, JAPANhoriuchi@faculty.chiba-u.jp

Abstract
In this study, we conduct a psychophysical experiment based on the hypothesis that c olour
information affects the spontaneous identification of copper. We created six types of coppermetal balls as test images using computer graphics. The surrounding c olour scene wasreflected on the balls of the test images. In each test image, we generated test stimuli in which
the colour gamut was expanded in ten steps, such that a part of the reflected colour wasdeviated from the colour gamut of the copper material. In our experiment, we checked theaccuracy and speed of copper material identification using rapid presentations. Experiments
have shown that colour distribution in an object contributes to the spontaneous perception ofmetals by humans. Participants identified copper materials in only a few hundred milliseconds,
and the colour gamut and speed of recognition were scene-dependent. In addition, it was foundthat the location where the colour gamut was expanded affected spontaneous perception.
Keywords: Material perception, Spontaneous identification, Metallic object, Visual experiment

1 Introduction
Metallic materials are common in our daily life. Copper has the same chromaticity as orange
and reddish brown, but it can be identified as a metallic c olour using higher-order physicalinformation associated with the gloss phenomena, such as spatial light reflection. However, as
we can instantly perceive that a material made of copper is metallic, it is natural to consider
that complex physical information is not processed in early vision.
Conventional studies reported that humans could quickly identify materials. Motoyoshi et al.
focused on the perception of surface gloss [1]. They hypothesized that simple image statistics
contributed to the identification of surface gloss and showed that the skewness of the luminancehistogram and sub-band filter outputs correlated to surface gloss and inversely correlated tosurface albedo. They also found evidence that human observers use skewness, or a similar
measure of histogram asymmetry, to judge the characteristics of surfaces. Sharan et al.
investigated the speed of material categorization [2]. They displayed images classified into nine
categorizes based on the material with short presentation times of 40, 80, and 120 ms, andfound that the categories could be identified quickly, requiring only 100 ms more than simple
baseline tasks. However, they did not investigate metal objec ts or discuss metal perception.
Based on both the results, it is expected that metallic objects can be identified using simple
image features.
In this study, we conducted a psychophysical experiment based on the hypothesis that c olour
information affects the spontaneous identification of copper materials. Reference [1] discussed
only luminance information for the perception of surface gloss. This is the first study toinvestigate colour information. In Ref. [3], a preliminary study was conducted. In this p aper, we
conduct analysis by increasing the number of test images.

2 Analysis of copper material
In Ref. [3], we measured the reflected colour of a copper object to analyze the properties of thecolour generated by metal objects. The X-Rite ColorChecker under the illuminant at atemperature of 6000 K was reflected on a real copper board, and spectral power distributions

Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALSof the original colour patch and reflected components were measured using a spectroradiometer(Konica Minolta CS-2000). Subsequently, we derived a spectral reflectance of the copper board
from the data. To simulate changes in the colour gamut from the original object to the reflectedcolour, we used the spectral reflectance database [4], which included 24 colours of the Macbeth
colour checker and 1269 colours of the Munsell colour chart. A partial dataset of the database
was used as the surface spectral reflectance of the original object. Using the derived spectralreflectance of the copper board, we calculated the spectral power distribution of the reflectedcolour component.
Figure 1 shows the changes in the colour gamut from the original colour to the reflected colour.
The green points represent the chromaticity of the original object, and the orange points
represent the chromaticity of the reflected component on the copper board. The coloured area
represents the colour gamut. The arrows extending from the chromaticity points show thechanges in chromaticity. The colour gamut of the reflected colour generally shrank comparedto that of the original object, and each metal type had a unique c olour gamut.

Figure 1 – Changes in the colour gamut

3 Experiment
Test stimuli
To verify whether colour information has an effect on the identification of metals, w e focusedon the colour gamut of the reflected colour component from a copper object. As the test stimulus,
we considered using either a real metal or computer graphics (CGs). Subsequently, in this study,
we decided to create a copper material image using CGs, which could freely set the colour.
The image was created using a 3D software (MAXON Computer, Cinema4D). The reflection
property of a spherical surface was provided by the Trowbridge -Reitz model (GGX). Only thespecular reflection was set with parameters of a metal sphere. The copper material in the layer
Fresnel preset was applied. Six types of high dynamic range images in Ref. [5] were used as
images of environment scenes. Therefore, six types of original scenes were generated. As
these original scenes exceeded the colour gamut of the copper board from the previous section,
the colour gamut was normalized such that 98% of all pixels would exist within the c olour gamut.
Figure 2 shows the normalized images. Further, in each spherical image, we generate d 10samples that extended the colour gamut of a specific object reflected in the image by extending
the distance from the white point to the chromaticity point of each pixel at equal intervals.
Scenes 1-4 have changed the colour of some of the objects, while scenes 5 and 6 have changed
the colour of the entire scene. The intervals were the ratio to the distance measured subjectively

Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALS

for each normalized image, as follows Table 1. In other words, when the colour gamut of thenormalized spherical image was 1, the ratio of the maximum colour gamut for each scene wasdetermined as follows: 6.4, 37.0, 5.5, 6.4, 4.6 and 4.6 for scenes 1, 2, 3, 4, 5 and 6, respectively.
The ratio of scene 2 was high because the allowable range of the colour gamut of the sky region
was large in the preparation experiment. Figure 4 shows a partial sample for scene 2.

(a) Scene1(b) Scene2(c) Scene3(d) Scene4(e) Scene5(f) Scene6

Figure 2 – Six types of normalized spherical images(a) Ratio = 1(b) Ratio = 13(c) Ratio = 25(d) Ratio = 37

Figure 3 – A partial sample for Scene2

Table 1 – Colour gamut expansion ratio

Min-Max

Scene1Scene2

Scene3

Scene4

Scene5

Scene6

1.0-6.4

1.0-37.0

1.0-5.5

1.0-6.4

1.0-4.6

1.0-4.6

Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALS

Step

0.6

4.0

0.5

0.6

0.4

0.4

Procedure
The experiment followed the procedure by Sharan et al. [1], but the presentation times were 40,80, and 120 ms. Their experiments aimed at categorizing materials into a single stimulus image.
This study aimed at the perception of metals using chromaticity change, and the presentation
time was too short to perceive differences among the stimuli. In our experiment, an initialstimulus was presented for 100, 200, 300, 400, or 500 ms. The initial stimulus was followed by
six perceptual masks appearing for 33 ms (Fig. 4). We created the masks using the PortillaSimoncelli texture synthesis method [6], which matches the statistics of the mask images to thestatistics of the stimulus images at multiple scales and orientations, thereby allowing for more
effective masking compared to the commonly used pink noise masks. Finally, a second stimulus
was presented for the same duration as the initial stimulus. In each trial, the task was to reportwhich stimuli were perceived as a copper material. All 45 combinations of 10 samples wereevaluated for each scene using two Alternative Forced Choice Task.
For all experiments, stimuli were displayed centrally on a calibrated liquid crystal display
monitor (ColorEdge CG-277, EIZO) against a gray background. The vertical scanning frequencyof the display was 61 Hz, and it included the AdobeRGB c olour gamut. The viewing distancewas 95 cm, and the viewing angle of the stimulus image was 12°, based on the experiment bySharan et al. [1]. The experiment was performed under fluorescen t lamps. Seven university
students with normal colour vision participated. Figure 5 shows the experimental setup.

Figure 4 – Procedure of the experiment

Figure 5 – Experimental environment

4 Results
Figures 6 and 7 shows examples of the experimental results by representing the Z-score forscene 2 and 5, respectively. The horizontal axis indicates the magnification rate of the c olour
gamut of 10 samples from the normalized image, and the vertical axis indicates the Z -value.
Smaller Z-value indicates that the stimulus is more likely to be perceived as copper. The
experimental results revealed the following:(1) Compared to the colour gamut of the reflected colours of real copper objects, stimuli could
not be easily identified as copper material when they were too narrow or wide.
(2) A reproduced colour image with a colour gamut slightly wider than that of the reflection of areal copper object was easily identified as a copper material.
(3) When the presentation time was shortened, identifying the c opper material was difficult. In
the case of complex reflection scenes, identifying the copper material was unstable for longer
presentation time as shown in Fig. 7. However, the colour gamut boundary identifiable as thecopper material was stable.

Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALS(4) The presentation time required to identify the copper material varied depending on thereflected surrounding scene.
The identification task was successfully performed when the presentation time was 300 ms forcomplex reflection scenes However, in simple reflection scenes, participants identified thecopper material in less than 100 ms, but it was unstable for the identification over 300 ms dueto the use of features higher than colour.

(a) 300ms(b) 400ms(c) 500ms
Figure 6 – Z-score for Scene 2

5 Conclusions
This study investigated the effect of colour information in the spontaneous perception of copper.
In our experiment, we checked the accuracy and speed of copper material identification using
rapid presentations. We confirmed that colour distribution in an object contributed tospontaneous perception of metals by humans. Participants identified copper materials in only afew hundred milliseconds, and the colour gamut and speed of recognition were scenedependent. In addition, it was found that the location where the colour gamut was expanded
affected spontaneous perception. In the future, studies should be conducted on verification ofdifferent metals in various scenes.

Ishiyama, H. et al. COLOUR CONTRIBUTION IN SPONTANEOUS IDENTIFICATION OF COPPER MATERIALS(a) 100ms(b) 200ms(c) 300ms
Figure 7 – Z-score for Scene 5

Acknowledgment
This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas (No.
15H05926) from MEXT, Japan.

References
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Sharan, L., Rosenholtz, R., & Adelson, E. H. 2014. Accuracy and speed of material
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Motoyoshi, I., Nishida, S., Sharan, L., & Adelson, E. H. 2007. Image statistics and theperception of surface qualities. Nature, 447, 206-209.

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Ishiyama T., Tanaka M., & Horiuchi, T. 2019. Contribution of color information in
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Barnard, K., Martin, L., Funt, B., & Coath, A. 2002. A Data Set for Colour Research, Color
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sIBL Archive, http://www.hdrlabs.com/sibl/archive.html.

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Portilla, J., & Simoncelli, E. P. 2000. A Parametric Texture Model based on Joint Statistics
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