Why printing?

Why was printing invented? Because of the plague. Between 1347 and 1400 waves of plague ravaged Europe, decimating the population by about 30 percent. Consequently there was a labor shortage and salaries rose.

This sparked an intensive demand for technological inventions, and printing, or more precisely moveable type, was one of them. The tedious and time consuming manual copying of documents no longer made economic sense when Hans Genssefleisch von Mentz, also known as Gutenberg, around 1400 started experimenting with type.

Fonts were just a rudimentary idea of individually carved wood blocks, but the enterpreneurial Gutenberg and a few skilled colleagues founded a financing partnership, which rised substantial capital to develop the new technology of mass-produced reusable metal font types.

It ended up taking 10 years until the venture was able to print the first documents using the new technology and sell them for a profit.

Could Gutenberg repeat his feat today?

Imaging Superatoms

A superatom is a composite of atoms, either homogeneous or heterogenous, that exhibits a similar electronic profile to a given single atom in the periodic table. Using photoelectron imaging, the figure below shows that a superatom of titanium oxide (bottom row) mimics the electron energetics of a single nickel atom (top row).


Previous experiments have shown that a cluster of 13 aluminum atoms behaves like a single iodine atom. Now, there appears to be a kind of arithmetic for superatoms. Here's how it works.

Another Fish Story from Justin Marshall

Cichlids [pronounced sik-lids] have several different cone opsin genes that enable them to detect light across the visible and ultraviolet regions of the spectrum. Different species express different subsets of these opsins to create alternate visual systems. Recent research has shown that cichlid fish in the clear waters of Lake Malawi expressed a wide range of opsins, with closely related species differing in whether they used the shorter wavelength or longer wavelength gene combinations.

PLoS Biology article: "The Eyes Have It: Regulatory and Structural Changes Both Underlie Cichlid Visual Pigment Diversity" by Christopher M. Hofmann, Kelly E. O'Quin, N. Justin Marshall, Thomas W. Cronin, Ole Seehausen4,5, Karen L. Carleton. (Not just another fish story)

Real Virtual Pages and Virtual Real Pages

From the in-box comes two different links to digital pages. In one case it's real virtual pages and in the other it's virtual real pages. From Udi comes a link to a set of collectible magcloud magazines relating to the movie Avatar. Which is interesting - a dynamic and timely publication of a physical artifact based on a movie largely constructed with computer graphics.



But what does this have to do with football you may be asking yourself?

Beet Spectrum

From Tim comes a fun site rawcolor.com which includes a page on using cabbage, beet and pumpkin juice as colorants in an inkjet printer.



What fun! But why stop with some test prints - what exactly does the spectrum of a beet look like?

Perceptual vs. colorimetric color spaces

In yesterday's post I noted that the authors had used a perceptual color space instead of a colorimetric one for their study. What is the difference?

It is already half a dozen years ago that I was assigned a project related to metamerism. To solve the open-ended problems, I implemented an extensive library for color science. For one specific solution I used a perceptual color space based on the McLeod's approach in making calculations using cone responses.

A principal color scientist then noted that perceptual color is used in vision research, but in engineering we use CIE based color spaces. Since my library was object oriented, all I had to do was to change a single import statement so that the CIE color matching functions (CMF) would be used as measures in the integration of the spectral data, instead of the cone fundamentals.

However, this minuscule engineering change has a big theoretical effect, because the CMF cover almost the entire visual domain, while the cone responses cover only a fraction of it. Form a signal processing point of view, the result is that that CIE color spaces have more signal modulation than perceptual spaces. Numerically the effect of this change in my code was small and irrelevant for the solution, but it is a big effect in terms of the underlaying science: when writing a paper the distinction is very important.

---

Now to my question.

I inherited a manuscript from a discouraged color scientist who gave up after years of rejection. I am now revising it in the hope of having it published, because the work has archival importance. The research is based on calculations in CIELAB. One of the reviewers writes:

"I object to […] their use of CIE modeling […], given that CIE did not design those models to serve as any sort of color appearance processing framework, but rather provided them as rules for engineers who want to reproduce chromaticities across different labs.

[…]

"Based on the history of the development and use of CIE, I believe it is suboptimal to model color appearance using CIE light mixture spaces (even the ones like CIELAB that claim to be approximately perceptually uniform). That is, using appropriate CIE chromaticities to report what stimuli are used in experiments is a fine practice, but for modeling and predicting human response to color it is best to go with psychophysically modeled spaces and the associated indices like Weber contrast, Michelson contrast or RMS contrast. To the degree that you continue to model human response with the CIE engineering emphasis, I think your results may be useful for rendering color in displays, but will tell us far less about the human response of [the studied effect]. I would suggest removing any CIE modeling of human response from your report and expanding explanations based on psychophysical modeling."

The implication here is that CIE colorimetry is not based on psychophysics. If I had the raw data, I might just have recomputed all quantities using cone responses, but unfortunately the raw data appears to be lost.

The only way out of this conundrum is to fully understand the difference between perceptual models and colorimetric models, then to provide a solid explanation. At this point I am seeking either an explanation or a good pointer to something I should read and understand.

Can you help?

Categorical color constancy for simulated surfaces

We all know about colorimetric color constancy, which for us in color imaging mostly takes the form of white point estimation and white balancing. The November 12, 2009 issue of the Journal of Vision has an interesting article by Maria Olkkonen et al. on a different kind of color constancy.

Color constancy in general refers to perceived color of objects not changing much when seen under different illuminations. In their paper Categorical color constancy for simulated surfaces, Maria Olkkonen, Thorsten Hansen, and Karl R. Gegenfurtner study the communication of color under different illuminations.

Color communication entails color naming and color naming entails categorical color. The authors start with the reflectance spectra of the Munsell color chips, calculate their tristimulus values under five different illuminations, and display them on a Sony Multiscan GDM-F520 monitor, whose gamut restricts them to limit the chips to 469 samples.

In a very diligently executed psychophysical experiment, four observers categorize each sample into one of the categories green, turquoise, blue, purple, red, orange, yellow, brown, or gray. The categories for orange and brown are pooled.

Since color matching is not involved, the authors are not using a colorimetric color space, but a perceptual color space, viz. the DKL color space, which is a linear transformation of the cone excitation space based on the Smith and Pokorny cone fundamentals. DKL is for Derrington, Krauskopf, and Lennie.

The observers perform the color naming experiment twice, with a 6 month hiatus, yielding a measure for the reliability of color naming. With this "calibration" the categorical color constancy is nearly perfect.

This result is important because the World Color Survey is performed under unrecorded random ambient conditions, a fact that has often been criticized. As we reported in our EI paper, Boynton wrote "the mechanisms of color constancy work so well that, within limits, the intensity and spectral distribution of the light used to illuminate the experimental materials make surprisingly little difference," but that was just an intuition. With their painstakingly rigorous experiment, Maria Olkkonen, Thorsten Hansen, and Karl R. Gegenfurtner deliver a definitive proof.

Some points to note:

• In rigorous experiments, you can only have a small number of conforming observers (see Mavericks are best for crowd-sourcing)
• When not doing color matching, a perceptual color space is better than a colorimetric one
• They limit the categories to nine color terms
• Yes Virginia, color naming via crowd-sourcing is legitimate

U.S. Share of World Research Community Declines

The UNESCO Institute for Statistics released a study on research last week. It provides new evidence of the global distribution of science capacity. According to the report, the number of individuals engaged in research worldwide grew from 5.8 million in 2002 to 7.1 million in 2007. As much of this growth was in developing countries, the U.S. share of the total declined from 23.2% to 20.3% and Europe's from 28.1% to 25.8%. China's share, meanwhile, grew from 14.0% to 20.1%. As a fraction of each nation's population, however, the U.S. still has more than 4.5 times as many researchers as China. The number of researchers in the developing world grew by a remarkable 56% between 2002 and 2007, while those in developed nations rose by 8.6%.

At the same time, expenditure on research and development (R&D) is increasing. Globally, the percentage of GDP (Gross Domestic Product) devoted to R&D has gone up significantly in most countries. In 2007, 1.74% of the world’s GDP was devoted to R&D (1.71% in 2002). While most developing countries invest less than 1% of their GDP in R&D, there are certain exceptions such as China (1.5%) and Tunisia (1%). The average rate of expenditure in Asia reached 1.6% in 2007, influenced by the top investors: Japan (3.4%), the Republic of Korea (3.5%) and Singapore (2.6%). In contrast, India invested only 0.8% of its GDP in R&D in 2007.

These results indicate that many countries are now recognizing the importance of innovation, in the broader sense. “Policy makers seem to realize more and more that innovation is key for economic growth, to the point of setting R&D investment targets,” notes Martin Schaaper, program specialist at the UNESCO Institute of Statistics, one of the authors of the study. “China is the foremost example of a country setting a target: 2% by 2010 and 2.5% or more by 2020."

More information on the UIS study