My Big Win in Vegas!

  (Send contributions to mbrill@datacolor.com)

When I find myself in a casino, it’s always because of a professional meeting. My reaction to these casino visits is perhaps predictable.

 

In 1979, I attended the Illuminating Engineering Society meeting in Atlantic City. There I dropped a coin in a slot machine, and then became so absorbed in a conversation with Bill Thornton that I was oblivious when a red carpet was unrolled behind me and Sammy Davis, Jr. walked from one end to the other.

 

In 1985, I attended the quadrennial AIC meeting in Monte Carlo. There I sacrificed another coin to a one-armed bandit, and then became so absorbed in a conversation with Claude Pelissier that I didn’t see the sights all around me on the “topless” beach.

 

In 1992, a field trip after an Acoustical Society meeting in Salt Lake City lodged me at an inexpensive casino/hotel in Mesquite, NV. A somewhat larger sacrifice went to a slot machine there. I was so impressed by the casino’s use of light and color to disorient patrons so they would gamble, that I presented a summary of the ruse at an ISCC Interest Group III panel discussion (see ISCC News Issue 340). Nobody told me what glamorous opportunity I missed in Mesquite, but I must have missed something because I got lost on the way back to my table from the restaurant salad bar due to the evil genius of the casino architects.

 

In 2005, I returned to Nevada, this time to an ASTM E12 meeting in Reno. By now my obligatory slot sacrifice swelled to five dollars. I’m reasonably sure I didn’t miss any glamorous sights, and I was aware that, in all my experiences with casinos, I hadn’t retrieved a penny from the slot machines.

 

But then, in September 2021, I visited Las Vegas and had my Big Win. The meeting this time (American Urology Association) was not mine but my partner Karen’s. After she made travel arrangements with me in tow, the AUA thought it prudent to make the meeting virtual. Karen was determined not to change our plans, so we checked into the Venetian, the conference hotel. (It was on the opposite end of the price scale from the hotel I’d stayed at in Mesquite in 1992.) Karen attended the virtual meeting from our hotel room. To me it was a bit eerie, like a séance with ghosts from an alternate universe. Meanwhile, I explored the hotel/casino complex. True to my experience in Mesquite, I became repeatedly lost on a grand scale. One small but effective confusion was to call two of the three hotels in the complex “Venetian” and “Venezia.” The architecture and a sinuous indoor canal spoke “Venice,” but straight paths and right angles were rare, and the site map had little visual correspondence to the site itself. Even employees of some of the concessions could not describe how to get to other places under the same roof.

 

Later, we embarked on an evening tour on a double-decker bus, and I had an opportunity to meet my obligatory slot machine. The tour guide gave us an hour in the environs of the Golden Nugget Casino. Stepping inside, I found that the only acceptable way to sit down for an hour was to gamble. Karen slid a $20 bill into a machine, and we managed to keep busy for more than a half hour. Her goal was to get as much run-time as possible out of that investment. She found that strategy was great fun when she visited Las Vegas with her mother several years earlier. When I slid my $20 bill into the same machine, I had a different goal. After a while I had won almost ten dollars, whereupon I decided to cash out. A little slip of paper appeared, and I grabbed it and asked the nearest bystanders where I could find the cashier prior to the imminent departure of our bus. Peering with cow-like eyes, they told me they didn’t know. How could anyone gamble without a plan for even the first two minutes that would ensue if they actually won? It was sad.

 

Out of time, I returned to the bus and read the paper slip from the machine: Void after 30 days! I’d have to come back to the Golden Nugget later in my trip. But I never did.

 

So that was how I scored my Big Win in Vegas. Well, maybe a Bigger Win was to have made the trip despite some significant health issues and with the overarching Covid risk. Some people thought I was crazy to do it. Maybe it’s good to indulge such craziness just once amid the larger-than-life habitués of Vegas.

 

Michael H. Brill

Datacolor

If CIECAM is the answer, what was the question?

 (Send contributions to mbrill@datacolor.com )

 

Imagine, while studying in preparation for his next life as a color scientist, the ghost of Alex Trebek visits us in his former role and announces his truly Final Jeopardy answer:

“CIECAM.”  

 

The contestants blink and Trebek explains:

“If CIECAM is the answer, what was the question?”

And the contestants answer:

 

Contestant 1: “What model predicts symmetric color matches?” WRONG: That was CIEXYZ.

Contestant 2: “What model predicts asymmetric color matches?” WRONG: That was CIECAT.

Contestant 3: “What model predicts color difference?” WRONG: That was CIECAM-UCS.

Contestant 4: “What model allows a stimulus, in given viewing conditions, to be numerically described with correlates of perceptual attributes such as brightness, lightness, colorfulness, chroma, and hue?” [1] CORRECT: Although CIE’s color-appearance models, CIECAMs, are not the only possible models.

 

Contestant 2: “That’s not fair! I’ve seen CIECAMs tested by asymmetric matches, but never by the elusive ‘numerically described perceptual attributes.’”

 

Contestant 3: “Well, come to think of it, Luo et al. [2] describe experiments to test people’s ability to use particular perceptual attributes: ‘For the memory matching method, observers are first trained using the Munsell colour order system (or some other suitable system) until they are very familiar with these scales (i.e., Munsell Value, Chroma, and Hue) … In the magnitude estimation method, observers are asked to make estimates of the magnitudes of some perceptual attributes (e.g., lightness, colourfulness, and hue). It is essential that each observer clearly understands the perceptual attributes being scaled.’”

 

Contestant 2: “It sounds as if those experiments tested the memorability and amenability for scaling of particular coordinates of a particular color-order system. They cannot make a statement about color appearance independent of the color-order coordinates chosen for training the subjects. How do you know one CAM is better than another if the subject’s training has such a bias? And I understand the precision of these tests is pretty low. I still think there is no match-free way to test a CAM—or for that matter, to use a CAM for color management. Alex is wrong and we should have a recount.”

 

Trebek: Well, it’s time for me to go now. This discussion is turning into a quagmire, and it looks like real color-management systems rely on asymmetric match predictions anyway. So let’s ask a professional organization like the ISCC to sort it out. Meanwhile, I’ll have to tell my game-show successor that the right question for CIECAM is “What color-management model is not out of Jeopardy?”

 

[1] M. D. Fairchild and L. Reniff, A pictorial review of color appearance models, 1997 SID/IS&T Color Imaging Conference, first paragraph of Introduction.

[2] M. R. Luo et al., Quantifying colour appearance part I. LUTCHI colour appearance data. Color Res Appl 16: 168-180 (1991).

 

Michael H. Brill

Datacolor