This is Part 4
Part 2: U.S. Army Scorpion Camouflage
Part 3: Why Not Just Use MARPAT?
(Vancouver, B.C. June 11, 2013) In January 2012, four companies and one U.S. Government pattern were announced for the down select in the U.S. Army’s Phase IV Camouflage Improvement Effort. (the US4CES patterns I developed for ADS Inc. is one of the four down selected). The winner is expected to be announced in a few days: June 14th (U.S. Army's Birthday).
In Part 1, I explained why the U.S. Army was conducting this Camouflage Improvement Effort.
In Part 2, I explained why the Government dropped their Scorpion submission from the competition with some focus on the Transitional baseline pattern - Multicam.
The Government has since revealed that their submission was not just the Scorpion pattern but they submitted three different geometries for each environment: a pixelated camouflage for Woodland, Desert All-Over-Brush for Arid/Desert and Scorpion for Transitional (1)
In Part 3, I answered the question Why not just use MARPAT? and how the U.S. Navy ground camouflage patterns: AOR1 and AOR2, became the Desert and Woodland baseline patterns for the U.S. Army testing and why the Army is looking for something better.
In Part 4, I will explain Why US4CES; how does it work, what does it do better...
The Army Phase IV effort requires a family of camouflage patterns with a common geometry and specialized colors for each environment of Woodland/Jungle, Arid/Desert, Transitional (between Woodland and Desert) and an option OCIE/PPE pattern if needed.
At "Army Day" in late 2010 where industry was invited to the ARL (Army Research Lab) to discuss Phase IV; the stipulation that this program would not be a fashion show was reiterated a number of times, meaning that aesthetics would not play a large role in determining the winner, they wanted effective patterns first and foremost.
After working on camouflage programs for the past 10 years with over 50+ countries, this was a welcome relief, I could focus on what works in camouflage rather than designing eye candy first and functionality second for the soldier. That is not to say that all our previous programs fall into that category but many programs would reject the effective pattern and go with a less effective camouflage with more pleasing aesthetics.
Based on the research I was aware of, had participate in, developed or was privy to on camouflage, I knew exactly what direction I would go in meet the Army's requirement. I knew I needed a better digital pixelated pattern than what had been issued to date. While these pixelated patterns had routinely done very well in military testing, I knew it could be done much better.
Natick Testing in 2009
Four environments were tested with a number of camouflage patterns and digital pixelated patterns did very well:
Woodland MARPAT was second place in the Rocky Desert terrain behind Multicam but no score is provided. We do know the score for Woodland MARPAT was below 83.3% and above 77.4%
Universal AOR tied Multicam for first place in the Mountainous Environments
AOR2 2nd place results for Cropland/Woodland are below, it came second to Woodland Digital (no score was provided for that pattern).
DCU Digital with Khaki PPE came in 2nd in Sandy Desert but DCU Digital with Matching PPE came in First (no score was provided for that pattern).
So given this information; three first place finishes and one in second, I knew a digital pixelated family could compete in every environment.
The first item I needed was the correct Macropattern, the large disruption blobs within a camouflage pattern, this is one of the most important factors in an effective camouflage.
Macropattern Algorithm: I developed the Macropattern Algorithm to generate a pattern which determines the most effective disruption for the target shape - in the case of US4CES I needed to camouflage a human shape. Prior to my Algorithm the only way to do determine the Macropattern properly had to be done by hand and require the geometric (mathematic) understanding on how to accomplish this properly with each target, now the algorithm determines the optimal disruptive Macropattern.
Note the minimal macropattern with MARPAT (below) and the much larger and more defined macropattern with US4CES
The target shape - (not just the outline of the target) should dictate your disruption points within the camouflage:
The larger macropattern in US4CES also allows for more effective camouflage at longer ranges than the tighter CADPAT/MARPAT/UCP/AOR1/AOR2 (which all use the same pattern). Their tight macropattern leads to an issue called isoluminance where the colors combine at a distance into one color and if that color is different from the background or brighter or darker than the background, then the soldier is easy to spot.
The Macropattern is designed to first fool with your Ambient Vision - outside of the focal area to stop or slow down the initial acquisition (detection) of the target. Pixelation (Micropattern) usually only tricks the focal region and has very little impact on the Ambient, so pixels are almost a failsafe, the key is delaying the Ambient (detection) to avoid the Focal (recognition).
There are a few other factors to work into this Macropattern development:
These are subsets of the Macropattern Algorithm.
Symmetry Disruption Algorithm: The Macropattern is positioned so the symmetry; left and right parts of the torso (or other parts of the target) do not match each other – the brain is very good at connecting the dots and two similar portions, where the camouflage pattern on the left and right side match, this will allow for easier acquisition. (5)
Symmetry Axis Algorithm: Horizontal, Vertical, Angular or Random Flows have a huge impact on the visibility of a pattern. Every target has a Symmetry Axis (SymAx = simplified shape – like a stickman for the human shape) which needs to be properly chopped up in the correct direction. If the flow is running with the SymAx then the brain is able to associate the target shape much easier, if the pattern runs counter to the SymAx then it makes it more difficult to determine the shape.
Below, a CF-18 (Canadian F-18) in current grey camouflage (left), note the false canopy painted on the bottom is a deceptive form of camouflage which tricks the opponent in dog fights. I added the three color "Thunder" pattern in the center photo showing how the left and the right side (Symmetry) are now much more difficult to put the pieces together to allow the brain to interpret what it is looking at (initial acquisition is delayed and identification that it is a CF-18 is more difficult) however, the straight lines within the pattern, stand out once in the focal vision locks on the target. The third image (right) shows how digital pixelation of the Lightning pattern now called "Digital Thunder" improved the effectiveness (difficulty in identification) over the same Thunder pattern making it more difficult for the brain to identify the target against the background.
Pixels have minimal impact on the ambient vision but a large impact on the focal cone causing further delay as the brain attempts to process the increased detail, when pixelation in camouflage is done correctly (color and scale) the brain will confuse the background noise with the pixels, removing the anomaly as a threat or delaying the identification of a threat. (6)
Our Digital Thunder pattern became a reality with the Slovakian Air Force Mig-29's
As well as our Cloudcam pattern
Movement Concealment Algorithm: The Macropattern is designed to disrupt not only the human shape but also to mask movement; key points within the pattern will disrupt the pivot points of the limbs and torso which when done incorrectly makes the patterns target shape easily identified when the target moves. One big issue is not only to make detection difficult but also identification – a soldier is unable to shoot at a target until he identifies what the target is.
Fractal Algorithm: To design and effective camouflage, natural geometric shapes must be applied; these are known as Fractals (feed-back loops).
A Fractal is a geometric shape which tends to repeat at larger or smaller scales within the same object. A twig is a small version of a branch which can be a small version of a parent branch which is a small version of the tree trunk. A twig close up may resemble the tree trunk at a distance and only size separates the two, if no reference to scale is provided the two may be indistinguishable from each other.
In the past Camouflage design has been a semi random placement of color and shape to disrupt the targets true shape, or camouflage patterns have attempted to mimic natural camouflage, in both cases these designs actually go too far in random patterns or specific mimicry to provide a better camouflage.
Our subconscious actually notices these fractal shapes and once the brain has identified them as natural and commonplace in particular settings the subconscious retains a memory of those shapes. When the visual part of the brain analyzes a setting, it quickly notes the common fractals it’s seen in the past and ignores them, treating them as background noise – not something which requires further scrutiny. If the pattern is not a fractal – such as a random placement of blobs, it may in-fact stand out to the visual system, the subconscious will note that something is out of place - an anomaly. If the pattern is to specific, such as many hunting patterns with trees and branches, this becomes easier to detect once placed in a different background than the camouflage was designed for.
A fern is a fractal - the small leaves that make up the large leaf of a fern are miniature versions of the larger leaf.
What I attempt to do with fractal camouflage is to merge these common shapes (using proprietary algorithms) with the correct background and can now be integrated this into the formula to come up with optimum patterns for each target rather than designing a pattern and then determining the object to conceal after the creation of the pattern.
Fractal camouflage is not about duplicating nature, but designing from the aspect of target shape, size, scale, operational environment, vision science, geometrics, algorithms, static or mobile target and color science. It really is rocket science.
Now we can address the Micropattern - digital pixels. These are required to add background noise and texture matching with the background and this is designed to fool with the focal area of the eye - when you are looking directly at or close to the target to make it more difficult to recognize what you are looking at. I have been involved in enough testing to know that the smaller digital pixel, while effective, could be improved upon with a larger scale, the tradeoff is in aesthetics, up close the pattern becomes chunky looking. The Army wants improvement in concealment, not the "best at show".
I was involved in developing the USMC Overwhite pattern in 2005, the pattern is now called "Snow MARPAT" (shown below). Initially the pattern and pixels were much smaller, only when the pattern was enlarged to the current scale did the military get the results that showed this pattern was more effective than all white uniforms. Note the similarity in scale of the pixels to US4CES. Snow MARPAT uses a different Macropattern than US4CES as I had not developed the Macropattern Algorithm at that time.
So much emphasis has been placed on the Micropattern (pixels) that the larger Macropattern has become lost in many of these previous patterns, thus these disruptive patterns now merge to appear as one solid color within tactical distances reducing their effectiveness.
A comparison of CADPAT TW with US4CES Woodland, Note the difference in Macropatterns between the two. US4CES was designed to continue to disrupt the target at farther distances than CADPAT/MARPAT/AOR1/AOR2... which all use the same print screens
So we are merging Macropattern - Fractal Geometry and Micropattern to determine our overall pattern.
Enhancing the pattern:
Boundary Luminance Gradient Algorithm: The darkest and lightest colors are strategically placed into a thin line or border to create a boundary luminance gradient between the two or three dominant color layers which creates a better disruption element between the two similar hues. Colorblind people perceive similar hues as the same color and the disruption effect is lost - the green hue of night vision has the same effect and the target shape is easier to detect, thus the boundary luminance gradient helps separate these colors even under colorblind conditions.
Three Dimensional Depth Algorithm: Using the lightest colors and darkest colors as a step in the correct location simulates a depth within the pattern and on the surface of the target further adding to confusion. If you look at a flat surface that can appear to have holes and raised portions the brain will interpret this as texture with depth and ignore the target as it is searching for the flat surface.
The image below shows US4CES with the merged Boundary Luminance Gradient algorithm and the Three Dimensional Depth algorithm, these are the thin boundaries which utilize the brightest color and darkest color to separate the the colors from each other, this will create a perceived difference in contrast. By strategically using either a light or dark boundaries we are now able to also create the three dimensional effect within the camouflage
The images and charts below show that this boundary luminance gradient effect has been well researched in vision science and the brain does indeed perceive higher contrast when in-fact none exists.
Contrast is critical to camouflage, but so is blending, so using this boundary gradient technique we can use colors that both blend better and still disrupt the target through perceived contrast.
Camouflage Color Algorithm: This Algorithm breaks down the primary colors within an environment to provide not only those dominant colors but also the colors which are required for proper simulation of shadows and reflections in the correct percentage. This algorithm will determine the proper amount of contrast required within a pattern so it doesn’t isoluminate –appear to break down into a single color at distance. The opposite is true if you oversaturated the colors and provide too much contrast – the pattern draws attention.
I wanted a fairly even percentage with all four of the US4CES colors. Where MARPAT has one very minor color (5%) and a very dominant color (48%), for a better disruptive effect I wanted a better balance out of the two brightest colors at 28 % and 29% based on the results I was seeing with the color algorithm, this allows for better blending and disruption.
These are environments between Woodland and Desert. Multicam (the baseline for the Army Phase IV testing) has proved effective in this terrain type allowing it to win the Army's Phase III testing in 2010 to replace UCP in Afghanistan. Multicam uses 7 colors, US4CES uses 4 colors, extra colors usually adds cost in manufacturing, so the question was, could we meet and exceed the effectiveness of Multicam with only 4 colors?
The Army had provided a series of test slides of the backgrounds they intended to sample to all the competitors in Phase IV. I used those slides to conduct our (ADS Inc.) own objective testing against the baseline patterns. Initially the pattern coloration I developed for Transitional (the US4CES OCIE below) only equaled Multicam within transitional environments, you can see from the similarity in coloration to Multicam why it only equaled Multicam in our testing.
After reviewing the test slides I decided that I would run all the transitional background slides through my Camouflage color algorithm a few different ways to see if I was missing something to determine the most predominant colors and with a tweak in the algorithm, I found that I required more green and less dark brown than my previous attempt. This time I found a coloration which was able to excel in transitional environments and the testing showed a marked improvement on the baselines.
The Army had asked for one OCIE pattern (vests and heavy nylon gear) to work across all three environments as it would be to expensive to produce this equipment in three colorations. So while most people would assume that our transitional would work as well for OCIE, I actually found that the green within the transitional was detrimental to our US4CES Arid submission, so I used our first transitional coloration as our OCIE submission.
Below (left and center left) is the US4CES Arid uniform with the US4CES OCIE on the vest and pouches. On the center right is US4CES Transitional with the same US4CES OCIE and the far right is US4CES Woodland with the same US4CES OCIE
Many people still believe that the M80 Woodland pattern is a good pattern, in Part 1 I showed how a number of U.S. Army studies had shown the pattern to be ineffective. The colors were also very dark for the soldier in the open, with US4CES Woodland I changed the colors based on the color algorithm for those regions. There is quite a difference between colors in Woodland Coniferous, Woodland Deciduous, Cropland, Tropical, Jungle... as such there will be a compromise within those settings to attempt to blend over a broader range. But why did we use black as one of the colors in US4CES Woodland?
Lack of Black in nature - common misconception
While black is not a natural occurring color, it is a color(or lack thereof) that the brain perceives as depth. In experimenting with grays in place of black we saw a critical loss in depth of our patterns.
Look at most environments up close and there is little if any black, now look at the same environment from a distance of 100 yards and you will note that shading, shadows and distance combine to make these area look black or close to black. Take a digital picture of the same environment, open the picture as a gif and look at the palate that was created; darks and black appear as the program recognized these colors in the picture. It is not just an illusion; these are the colors the brain sees although they aren’t actually there at the source. Distance from an object and combined shadows create perceived dark zones.
Picture on Left (below)is the Universal Camouflage Pattern (UCP). Same picture on right with black zones, as determined by our program, highlighted in yellow. Note the vest worn is the M80 woodland pattern with black, the UCP has no black, so the program does not see any depth in the UCP pattern but it does on the M80 vest. (22)
Black helps to trick the brain into seeing through those dark regions as it does in natural areas.
In a desert setting there are not going to be a great deal of shadows so large amounts of black are not useful in this pattern color and browns are used in place of black.
In woodland (temperate) areas where trees and bushes may be predominant, multiple shading and shadows lend to black as a color that will blend the uniform as the eye interprets those areas as shadows or holes to break the silhouette of the object that we are trying to conceal.
What you can't see can make all the difference
In the last 15 years, the Near Infrared Spectrum NIR (Near Infrared) and SWIR (Short Wave Infrared) has become an increasing requirement for camouflage to also be effective against Night Vision Devices (NVD's) or Night Vision Goggles (NVG's). How do you do this?
There are two key components to remember when designing for these spectrums - contrast in the spectrum, (color separation as far down the spectrum as possible) and color reflectance; does the green color in your camouflage accurately reflect foliage, does your brown match soil reflectance's, does your tan match the low desert reflectance in the NIR and the higher reflectance in the SWIR...
US4CES has color separation much (much, much) farther down the spectrum than required (in other words, we knocked it out of the park and then some). The trick is to achieve this NIR/SWIR reflectance matching with the environmental background as well as color separation without compromising on the visual effectiveness of the camouflage.
Desert is very difficult to achieve both the desired brightness to match the desert in the visual spectrum and the low reflection of the desert at night within the NIR. The old 3 color desert - DCU camouflage places at or near the top in visual testing but reflects much brighter than the background at night in the NIR and so that pattern is no longer a valid option for the U.S. Military. MARPAT Desert coloration then began to show improved NIR results and AOR1 improved this one step further making it the baseline for the Army's Desert Camouflage.
The two red lines in the chart below show the Minimum and Maximum allowable reflectance for Desert camouflage within the NIR spectrum.
In Part 3, I showed how MARPAT desert was developed and which direction the Canadians chose to go based on their Arid/Desert testing; a larger Macropattern was required as well as colors that work in the NIR. For the US4CES pattern, I could not change the geometry according to the requirement to provide a larger Macropattern for the desert (all three patterns needed the same geometry), so with US4CES Arid I was able to achieve the perceived openness of the CADPAT AR using very similar colors in two layers to make these two layers look like one similar layer at tactical distances which is right around 50% of the US4CES pattern.
While these two colors in US4CES Arid look similar in Hue in the Visual Spectrum, they separate in the NIR for better disruption than AOR1 (see photo below) all four colors separate in US4CES in the NIR which is what produces greater effectiveness at night against and enemy with Night Vision Devices. You also require a low reflectance and the OCIE pattern coloration has to come close to the ARID in the NIR and SWIR
While I wanted colors for US4CES Arid that was similar to the colors used for the U.S. Navy AOR1 in the visual spectrum, I wanted the NIR to come close to the CADPAT AR (Arid Regions) as seen below. There is a reason why the Canadians switched colors from their Trial Desert uniform pattern (very close to MARPAT Desert) to their issued CADPAT AR.
For the US4CES Woodland I wanted to exceed one of the best patterns in the NIR - CADPAT TW
And exceed the color separation to that of British DPM (below) once again we have four color separation into the NIR and beyond. The boundary luminance gradient I used on US4CES also has the same effect in the NIR.
For US4CES Transitional (below) I didn't want the same high contrast as the US4CES Woodland and I also wanted the green color to pop into the higher reflection that matched the foliage reflection within the NIR. The brightest color with Transitional in the NIR is not the light beige but the Green which is actually the third darkest color in the visual spectrum out of the four used.
In Part 3, I explained the results that while MARPAT Woodland is an effective pattern in the visual spectrum it is to bright in the NIR and lacks color separation, yet the Canadian CADPAT TW, matched the NIR reflection. The photo below confirms those results.
Below you can see in the photo on the left that the two patterns don't look that different from each other in color selections with the 3 darkest colors in the US4CES, but those slight differences have a massive impact on how they work in the NIR spectrum. Not only does US4CES Transitional now match the reflection criteria but we have also retained color separation and the three dimensional effect is still present.
Our US4CES Transitional is very close to CADPAT TW in the NIR (below), this is a good thing as it means our reflection level should be within the tolerance that CADPAT TW achieved which matches the required background reflection (as discussed in Part 3) we also have greater color separation which would indicate that US4CES Transitional works past 1450 nm, this is where the two greens in CADPAT TW merge into one color. (see part 3)
The US4CES Family in NIR
Could this be the deciding factor that determines the winner of Phase IV? Without this solution, you don't own the night!
We were allowed by the U.S. Army to submit two families, the one that made the finals is US4CES-A (Alpha), the family that was rejected was US4CES-D (Delta). What's the difference between the two?
The Geometry was identical, the colors were identical, the only change was merging the four colors so they flowed into each other. Only the US4CES-D Arid pattern was able to exceed the baseline AOR1 whereas the other preformed poorly in transitional and woodland not exceeding or meeting the baseline pattern requirements. To me this speaks volumes about this design trait.
Digital pixelated camouflage has always been counterintuitive, you are attempting to match nature with a very unnatural looking method, this throws allot of people off but it works, it has been tested by militaries around the world and you need to see it at proper distances (at a distance ranging from 30 to 350 meters)(29) for the effects to work.
US4CES was developed from scratch, utilizing just about every algorithm I had developed to provide the most effective camouflage I was able to design. In retrospect, after 2+ years since US4CES began development, if someone asked me, "would I have done this pattern different?", the simple answer is no.
US4CES was designed to protect the soldier, end of story, if the Army asked for the best looking pattern, there are a few hundred different patterns I would have chosen over US4CES, but that is not what they wanted and when lives depend on the functionality of the design, it's better to not compromise effectiveness for appearance sake.
This is Part 4
Part 2: U.S. Army Scorpion Camouflage
Part 3: Why Not Just Use MARPAT?
For more Camouflage news go to the HyperStealth® Home Page
2) SOLDIER CAMOUFLAGE FOR OPERATION ENDURING FREEDOM (OEF): PATTERN-IN-PICTURE (PIP) TECHNIQUE FOR EXPEDIENT HUMAN-IN-THE –LOOP CAMOUFLAGE ASSESSMENT, U.S. Army Natick Soldier Center. http://www.dtic.mil/dtic/tr/fulltext/u2/a532947.pdf
25) CONCEALMENT OF THE WARFIGHTER’S EQUIPMENT THROUGH ENHANCED POLYMER TECHNOLOGY http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA433437
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