5. CONCLUSIONS

conducted in the HSI color space. The hue denoising is implemented with a new weighted orientation diffusion that preserves color edge better than previous orientation diffusion. A modified curvature flow is applied in the saturation denoising. The intensity diffusion is our major contribution in this framework, which combines a GVF-based component and a fourthorder filter in one PDE. This combination employs the favorable features of both the GVF and the fourth-order term to implement a robust and accurate denoising process, and to overcome the staircase effect in smooth regions. The performance of the proposed algorithm has been assessed quantitatively and qualitatively in the experimental comparison with other recognized approaches and commercial software. The results indicate that the proposed approach accomplishes our goal and is competitive with the current state of the art.

REFERENCE

[1]L. Alvarez, F. Guichard, P.L. Lions, and J.M. Morel. Axioms and fundamental equations of image processing. Archive for Rational Mechanics and Analysis, 123(3):199-257, 1993.

[2]A.L. Yuille, T. Poggio. Scaling theorems for zero-crossings. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):15-25, 1986.

[3]A.P. Witkin. Scale space filtering. In Proc. International Joint Conf. Artificial Intelligence, pp.1019-1023, 1983.

[4]P. Perona, J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639, 1990.

[5]G. Gilboa, N. Sochen, and Y. Zeevi. Image enhancement and denoising by complex diffusion processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8):1020-1036, 2004.

[6]D. Tschumperle, R. Deriche. Vector-valued image regularization with PDEs: a common framework for different applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):506-517, 2005.

[7]R. Malladi, J.A. Sethian. Image processing: flows under min/max curvature and mean curvature. Graphical Models and Image Processing, 58(2):127-141, 1996.

[8]L. Alvarez, L. Mazorra. Signal and image restoration using shock filters and anisotropic diffusion. SIAM Journal of Numerical Analysis, 31(2):590-605, 1994.

[9]R. Carmona, S. Zhong. Adaptive smoothing respecting feature directions. IEEE Transactions on Image Processing, 7(3):353-358, 1998.

[10]J. Weickert. Coherence-enhancing diffusion filtering. International Journal of Computer Vision, 31(2/3):111-127, 1999.

[11]B. Tang, G. Sapiro, and V. Caselles. Color image enhancement via chromaticity diffusion. IEEE Transactions on Image Processing, 10(5):701-707, 2001.

[12]L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.

[13]P. Blomgren, T.F. Chan. Color TV: total variation methods for restoration of vectorvalued images. IEEE Transactions on Image Processing, 7(3):304-309, 1998.

[34]Y. You, W. Xu, A. Tannenbaum, and M. Kaveh. Behavioral analysis of anisotropic diffusion in image processing. IEEE Transactions on Image Processing, 5(11):15391553, 1996.

[35]F. Catte, T. Coll, P.L. Lions, and J.M. Morel. Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal of numerical analysis, 29:182-193, 1992.

[36]L. Alvarez, P.L. Lions, and J.M. Morel. Image selective smoothing and edge detection by nonlinear diffusion (ii). SIAM Journal of numerical analysis, 29:845-866, 1992.

[37]P. Kornprobst, R. Deriche, and G. Aubert. Image coupling, restoration and enhancement via PDEs. In Proc. IEEE International Conf. on Image Processing, pp.458-461, 1997.

[38]P. Kornprobst, R. Deriche, and G. Aubert. Nonlinear operators in image restoration. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.325-330, 1997.

[39]G. Gilboa, N. Sochen, and Y Zeevi. Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Transactions on Image Processing, 11(7):689-703, 2002.

[40]S. Osher, L. Rudin. Feature oriented image enhancement using shock filters. SIAM Journal of Numerical Analysis, 27(4):919-940, 1990.

[41]P. Perona. Orientation diffusions. IEEE Transactions on Image Processing, 7(3):457467, 1998.

[42]G. Sapiro, D.L. Ringach. Anisotropic diffusion of multivalued images with applications to color filtering. IEEE Transactions on Image Processing, 5(11):1582-1586, 1996.

[43]T. Chan, S. Osher, and J. Shen. The digital TV filter and nonlinear denoising. IEEE Transactions on Image Processing, 10(2):231-241, 2001.

[44]T. Chan, C. Wong. Total variation blind deconvolution. IEEE Transactions on Image Processing, 7(3):370-375, 1998.

[45]L. Rudin, S. Osher. Total variation based image restoration with free local constraints. In Proc. IEEE International Conf. on Image Processing, Vol. I, pp.31-35, 1994.

[46]L.A. Vese, S. Osher. Image denoising and decomposition with total variation minimization and oscillatory functions. Journal of Mathematical Imaging and Vision, 20:7-18, 2004.

[47]S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud. Variational approach for edgepreserving regularization using coupled PDE’s. IEEE Transactions on Image Processing, 7:387-397, 1998.

[48]S. Gao, T.D. Bui. Image segmentation and selective smoothing by using MumfordShah model. IEEE Transactions on Image Processing, 14(10):1537-1549, 2005.

[49]R. Malladi, J. Sethian, and B. Vemuri. Shape modeling with front propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2):158-171, 1995.

[50]R. Malladi, J. Sethian. Image processing: flows under min/max curvature and mean curvature. Graphical Models and Image Processing, 58(2):127-141, 1996.

[51]D. Mumford, J. Shah. Boundary detection by minimizing functionals. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.22-26, 1985.

[52]D. Mumford, J. Shah. Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42:577-685, 1989.

Chapter 8

COLOR IN PSYCHOLOGICAL RESEARCH: TOWARD

A SYSTEMATIC METHOD OF MEASUREMENT

Roger Feltman and Andrew Elliot

University of Rochester, Rochester, New York, USA

Psychology is a discipline that prides itself on being an empirical science. As such, rigorous statistical and methodological controls must be used to ensure the validity of every result. Ostensibly, each submission for publication is peer reviewed, and needs to be replicated by other scientists in other locations to confirm or disconfirm the results. This is how a scientific discipline must operate if it wishes to produce meaningful, accurate results. When a discipline strays from these procedures, it leaves itself open to criticism and more importantly, to the possibility of inaccurate or misleading conclusions. All research needs to ascribe to these standards, regardless of how time consuming, inefficient, or difficult they may be.

One area of research that has failed to live up to these standards is the study of color and psychological functioning. The aesthetic property of color may at first consideration make it seem like a trivial topic for study, but recent research (Elliot, Maier, Moller, Friedman, & Meinhardt, 2007) indicates exactly the opposite. Color has been shown to influence affect, cognition, and behavior (Elliot, Maier, Binser, Friedman, & Pekrun, 2009; Elliot et al., 2007; Lichtenfeld, Maier, Elliot, & Pekrun, In Press; Maier, Elliot, & Lichtenfeld, 2008). The degree and type of influence has varied from study to study, some more psychologically consequential (e.g. color and performance: Elliot et al., 2007) than others (e.g. shoe color preference: Trinkaus, 1991). None of these results, however, can be considered valid if they fail to live up the methodological rigors of science.

An in depth examination of the color research of the past and present makes it clear that most of the work fails to meet scientific standards (Fehrman & Fehrman, 2004). Too many studies have failed to take into account the three basic properties of color. Others have failed to consider the unconscious associations people have with color that could act as possible confounds. Stated differently, color used in an experiment may affect the experiment’s dependent variable in unwanted and unaccounted for ways. In either case, it is impossible to draw meaningful conclusions from these studies, as their results could be due to any number

Additionally, three potentially unanticipated factors come into play when color is used as an independent variable in research: 1) Combinations of colors may be perceived differently than each color would be observed in isolation (e.g. Kjelldahl & Schenkman, 2007). Yellow may look different when presented in the context of blue and red, for example. 2) The lighting of a space has a tremendous impact on how the colors are perceived (Hunt, 1991). A red in natural daylight will appear quite different than a red under fluorescent light; and 3) Any experiment with color must take into account possible color associations participants bring with them into the lab (Kaya & Epps, 2004). As will be seen, color associations are as diverse as they are many and vary from culture to culture (e.g. Hupka, Zaleski, Otto, Reidl, & Tarabrina, 1997). It is important to consider the effect of these associations when interpreting results.

Before examining the shortcomings of previous research, brief mention should be made of the various color models currently in use. Some research (mostly in the physiology and perception areas) has already implemented the color controls mentioned above. One such method is the Munsell color model (Munsell, 1905). This model assigns notations to every color based on lightness, chroma, and hue. In this way, researchers using the Munsell Model make use of a color’s previously defined properties which are documented in the Munsell Book of Color (Munsell, 1905). A brief literature search reveals its position as the prevalent color model in use today in research on color physiology and color perception (e.g. Choo & Kim, 2003; Franklin & Davies, 2004; Indow, 1999; Kaya & Crosby, 2006; Lindsey & Brown, 2002; Perez-Carpinell, Camps, Trottini, & Perez-Baylach, 2006; Rodrigues & Murre, 2007). Alternatives to the Munsell model include the LCH color system described above (Wyszecki & Stiles, 2000) as well as the Lab color space (Wyszecki et al., 2000). The latter two color systems were both developed by the International Commission on Illumination (CIE).

The preceding discussion has highlighted the complexity of color and outlined the characteristics that must be considered when conducting research involving color. Several studies will now be evaluated to demonstrate the shortcomings of prior psychologically-based research on color. The purpose of this review is not to simply reject the results of each study. Instead, the aim is to use past research to provide clear examples of how the principles detailed above can be used to improve research on color psychology.

One of the most studied issues in the color psychology literature is people’s color preferences (e.g. Coad & Coad, 2008; Dittmar, 2001; Ellis & Ficek, 2001; Eysenck, 1941; Guilford & Smith, 1959; Ou, Luo, Woodcock, & Wright, 2004; Rosenbloom, 2006; Wijk, Berg, Sivik, & Steen, 1999). The aim of investigating color preference varies across researchers. Some are interested in how color preferences indicate or influence personality (Lange & Rentfrow, 2007) while others seek to understand how people with psychopathologies differ in their color preferences from those without psychopathology (Strenski, Payson, Muzekari, & Bohr, 1970). Still others are interested in making a profit using color preference information, such as marketers (Singh, 2006) and advertisers (Lichtle, 2007). Color preferences have also been studied across age groups (Terwogt & Hoeksma, 2001; Zentner, 2001) and across cultures (Garth & Collado, 1929; Saito, 1996).

Unfortunately, most of the early research in this area, and most contemporary research as well, used methods that fail to yield conclusive results. For instance, in an early color preference study by Washburn (1911), the researcher states that different “tints” (presumably lightness values) and saturations were used. However, no mention was made of how tinted or saturated each color was. The researcher can be commended for his efforts to consider the

study. A study by Weller and Livingston (1988) may be used in representative fashion. In this study, participants were presented with questionnaires on pink, blue, or white paper after reading a vignette about a crime. They were asked to indicate their thoughts about the crime, the judgment (guilty or not guilty), and their thoughts about the defendant. It was found that the pink questionnaires produced less emotionality than the blue or white questionnaires. Were the results due to differences in hue or did the brightness or saturation of the pink (or blue) produce the differences? Without knowing the specific properties of the colors used, it is impossible to know; clear results will depend on a replication with color carefully controlled. (An interesting aside is that the authors of this study mentioned that the brightness contrast between the ink and the paper was not the same for the blue paper as for the pink and white papers, pointing to an additional, idiosyncratic, confound).

Color and personality is another area that has been extensively studied with little concern for appropriate color controls. The care and fastidiousness with which personality researchers approach various aspects of personality structure and process has not generalized to their use of color. For instance, the Color Pyramid Test (e.g. Schaie, 1963) is a non-verbal test of personality that asks participants to arrange colored squares to make both “pretty” and “ugly” pyramids. The analysis of the composition of these pyramids, which is based exclusively on hue, is presumed to reveal the respondent’s personality. However, none of the colors used in the Color Pyramid Test have had their lightness or chroma measured. Thus, choices perceived to be made with regard to hue, may actually be made with regard to lightness, saturation, or an interaction of color properties. Another personality test using unstandardized colors is the Lüscher Color Test (Luscher, 1969). Respondents chose colors ranging from most favorite to least favorite and a respondent’s choices are said to reflect his or her personality. Again, all analyses are made based on uncontrolled hues, and no consideration is given to the other properties of color.

These color-based personality trait tests are not widely used by researchers and clinicians today and their validity is strongly doubted (Fernando, Cernovsky, & Harricharan, 1992; Picco & Dzindolet, 1994), but their popularity remains high among the general public. It is common to hear people “diagnose” their or their friend’s personality based on their favorite color. The internet is also filled with personality tests based on color (Fuentes).

Researchers have also been interested in how color is able to influence or alter emotional states in work and retail environments. It has long been claimed that red is an exciting color while blue is calming in such settings (see: Fehrman et al., 2004). What have researchers found? Yildirim, Akalin-Baskaya, & Hidayetoglu (2007) found that violet interiors were preferred to yellow in a restaurant setting. Kuller, Mikellides, & Janssens (2009) found that when employees subjectively rated the “colorfulness” of their work environments, those in the most colorful work spaces reported the highest degree of emotionality (e.g., happiness). Bellizzi and colleagues (1992) found that blue retail environments produce more positive customer feelings than red retail environments. Bellizzi, Crowley, and Hasty (1983) report that “warm” (long wavelength) colors draw people in closer, but are seen as less pleasant than “cool” (short wavelength) colors. None of these studies has used proper procedures to control for non-hue properties of color. Nevertheless, in the past decade the retailing industry has become very interested in the effects color can have on shopping behavior. Entire articles have been devoted to reviewing how to use color most effectively (e.g. Singh, 2006), and an issue of the Journal of Business Research has concentrated on the aesthetics used in retailing, including to a large extent, the use of color (August 2000).

Words like despair, defeated, and pathetic were presented in a variety of colors, one of which was red. Given the documented link between red and failure (Moeller et al., 2009), red likely influenced responses to these achievement-relevant words, unbeknown to the investigator. A second study utilized the Stroop task in investigating attentional bias for smoking-related and affective words in smokers (Drobes, Elibero, & Evans, 2006). The word “failure” was used as a target word in the study, as was the color red. Again, given the red-failure link, this color-word association undoubtedly influenced participants’ responses with unknown effects on the final results obtained.

It is encouraging to note that some researchers, particularly in the sensation and perception area, have realized and attended to the importance of strict color measurement. Almost all of the recent work submitted to Color Research and Application, for instance, has included quite strict color controls (e.g., Küller et al., 2008). This rigor, however, remains absent even in research published in such premier journals as Science. For example, Mehta and Zhu (2009) recently published a paper showing that red increases performance on lowlevel, detail oriented tasks and that blue increase creativity. These results have been widely disseminated in the public arena, and are likely accepted as fact by most who hear that the work was published in Science. Unfortunately, this work did not include proper procedures for equating hues on lightness and chroma. This is not to say that the results are necessarily incorrect (see Maier et al., 2008, for similar predictions), but it remains unclear, at present, whether the findings are indeed due to variations in hue rather than other properties of color.

Now that the deficiencies in much extant research on color and psychological functioning have been reviewed, we turn our attention to how to address the challenges presented by color research. A straightforward and effective way to control for color properties is to use a device known as a spectrophotometer. This device is able to provide precise information on lightness, chroma, and hue using data at the spectral level. Using a spectrophotometer, each and every color used in an experiment can be individually tested and verified (see: Lee & Honson, 2003). This is true regardless of whether the color is printed on paper or displayed on a computer monitor. Operating the spectrophotometer is not difficult, but it does require some investment of time to learn the proper procedures. In addition to being rather straightforward to use, specrophotometers are able to produce output in several different color spaces. Switching from an lcH color space to another CIE color space presents no problems for the researcher, and can be very helpful in comparing colors used in various published articles.

It should be noted that as in all research, putting method into practice is more difficult than might appear on the surface. While it is indeed easy to obtain spectral data on color stimuli, it is more difficult to ensure that the hues used in an experiment have the same lightness and saturation (or, more generally stated, that only one property of color varied at a time). A small change in the paper on which the colors are printed, the ink used to print the colors, and the lighting of the room in which the measurements are taken, can all impact the readings of the spectrophotometer. It is thus necessary to enforce strict controls over all of these factors: Ensure that the paper (or whatever material the color is being printed on) is of high quality and from the same packet for different colors, use ink manufactured in the same batch within a given printing of stimulus materials, and measure the colors in a room in which the lighting is consistent. Failure to account for any of these variables could result in the same unwanted variation present in the research reviewed earlier.