Randomly Generating Color Sets with a Minimum Perceptual Distance

Posted on October 27, 2018

Earlier this year, I released a color cycle picker that enforces a minimum perceptual distance between colors, including color vision deficiency simulations, with the goal of creating a better color cycle to replace the “category 10” color palette used by default in Matplotlib, along with other data visualization packages. While the picker works well for what it was designed for—allowing a user to create a color cycle—it requires user intervention to create color sets or cycles.1 The basic technique used—performing color vision deficiency simulations2 for various types of deficiencies and enforcing a minimum perceptual difference for the simulated colors using the CAM02-UCS3 perceptually uniform color space (where each type of deficiency is treated separately) and a minimum lightness distance (for grayscale)—is still valid for the random generation of color sets; it just needs to be extended to randomly sample the color space.

A few different color sets
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  1. A color set doesn’t have a defined order, while a color cycle does. ↩

  2. G. M. Machado, M. M. Oliveira, and L. A. F. Fernandes, “A Physiologically-based Model for Simulation of Color Vision Deficiency,” in IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1291-1298, Nov.-Dec. 2009. doi:10.1109/TVCG.2009.113  ↩

  3. Luo M.R., Li C. (2013) CIECAM02 and Its Recent Developments. In: Fernandez-Maloigne C. (eds) Advanced Color Image Processing and Analysis. Springer, New York, NY. doi:10.1007/978-1-4419-6190-7_2  ↩

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3D-Printed Hilbert Curve Absorbers

Posted on August 11, 2018

Fused filament fabrication (FFF) 3D printers are good for many things,1 but production of sharp points is not among those strengths. Thus, the traditional structure of millimeter wave absorbers—a periodic array of square pyramids—is poorly suited for production via FFF printers. Millimeter wave absorbers serve a crucial role in Cosmic Microwave Background (CMB) telescopes by terminating stray light, which is necessary for reducing systematic errors. Not only are the points difficult to produce; they’re also fragile, since a print is generally weakest along its layer lines. Thus, a new geometry was needed, as is detailed in my paper titled A 3D-printed broadband millimeter wave absorber.

Space-filling curves such as the Hilbert curve completely fill the unit square. Furthermore, the Hilbert curve can be described by a sequence of physically realizable geometric approximations. Tracing a wedge along such an approximation creates a geometry with similar optical properties as a pyramidal array but with enhanced mechanical robustness. Importantly, this geometry can be printed without repeatedly starting and stopping extrusion, making it well suited for FFF printers.

Hilbert Curve Absorber (Detail) Continue reading →

  1. A LulzBot TAZ 6, in this case. ↩

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Geysers del Tatio

Posted on June 30, 2018

I recently returned from a couple months working in Chile. While there, I finally made it out to see the El Tatio geyser field, which is the third largest geyser field in the world. The geysers are around an 80 km, hour and a quarter drive north of San Pedro de Atacama and are normally seen just before sunrise. Some photos I took are below.

Geyser Field Continue reading →

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Photogrammetry Targets

Posted on May 26, 2018

The Scanreference photogrammetry system includes 149 magnetic coded targets and PDFs for printing 192 more targets. However, while measuring something that isn’t ferromagnetic, the magnetic targets aren’t particularly helpful, and the 192 printable coded targets aren’t always enough. Unfortunately, AICON wouldn’t provide the full set of printable coded targets when I asked and instead tried to sell me a multi-thousand dollar software package for generating printable coded targets. Instead, I looked in the literature and found multiple references to a 1991 paper1 as the original publication about the ring code targets. Unfortunately, the paper is not available electronically, so I had to request a copy via interlibrary loan; I received a copy just to find out that it didn’t include any technical details on the targets.

Fortunately, further research turned up expired German patent DE19733466A1. The patent contains all of the details needed to generate the ring codes for the coded targets, except for the exact parameters and numbering scheme used for the Scanreference targets. This missing information was fairly straightforward to figure out—the targets are 14-bit with no restrictions on the number of transitions from black segments to white segments and are ordered by increasing binary value. With this information, I was then able to write a script to generate the ring codes and a script to generate a set of printable targets, resulting in a PDF with all 516 targets ready to print on stickers.

  1. Schneider, C. T. “3-D Vermessung von Oberflächen und Bauteilen durch Photogrammetrie und Bildverarbeitung.” Proc. IDENT/VISION 91 (1991): 14-17.  ↩

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Color Cycle Picker

Posted on March 30, 2018

The “category10” color palette, originally developed by Tableau, was adopted as the default color cycle for Matplotlib 2.0 and is also used by default by D3.js and Vega, along with other software packages. While more aesthetically pleasing than the old Matplotlib default, it is unfortunately not colorblind-friendly.1 In an effort to improve this and promote the development of colorblind-friendly color cycles for scientific visualization, I built a color cycle picker that incorporates color vision deficiency simulation and enforces a minimum perceptual distance between colors, for both normal and anomalous trichromats. This is accomplished by performing color vision deficiency simulations2 for various types of deficiencies and enforcing a minimum perceptual difference for the simulated colors using the CAM02-UCS3 perceptually uniform color space (each type of deficiency is treated separately). Additionally, a minimum lightness distance is enforced, for better grayscale printability. The tool allows colors to be picked from a visualization of the CAM02-UCS color gamut and assembled into a color cycle. This visualization is performed using hardware-accelerated WebGL to allow for real-time interactive adjustment of parameters; the resulting palette is also visualized. The minimum perceptual color distance, lightness distance, and color vision deficiency simulation parameters are all adjustable. A hosted copy is provided, and the code is available in a repository on GitHub.

Color Cycle Picker Screenshot

  1. Personally, I have difficulty telling the second and third colors apart.  ↩

  2. G. M. Machado, M. M. Oliveira and L. A. F. Fernandes, “A Physiologically-based Model for Simulation of Color Vision Deficiency,” in IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1291-1298, Nov.-Dec. 2009. doi:10.1109/TVCG.2009.113  ↩

  3. Luo M.R., Li C. (2013) CIECAM02 and Its Recent Developments. In: Fernandez-Maloigne C. (eds) Advanced Color Image Processing and Analysis. Springer, New York, NY. doi:10.1007/978-1-4419-6190-7_2  ↩

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