Against Late Fall Hardwoods, Vias scores 58/100 (), while Deep Cover scores 37/100 ().
Based on color alignment, breakup scale, and texture density, the AI sees an approximate 21-point lean toward Vias in this particular environment.
Kuiu Vias and Forloh Deep Cover are both mixed-scale patterns, so they behave similarly from a scale point of view. Both patterns balances micro and macro elements, keeping them fairly steady across different shot distances. They are also similar in overall density, so neither one is dramatically busier or more open. Forloh Deep Cover carries a wider spread in scale elements, which can help it stay effective both up close and as animals get farther out.
Kuiu Vias vs Forloh Deep Cover
Kuiu Vias and Forloh Deep Cover have been analyzed using our CamoMatrix AI engine, which measures scale, density, and edge behavior directly from the flat pattern artwork. Both land in the mixed-scale category, meaning they balance fine texture with larger breakup blocks instead of living at one extreme. Density is similar, so neither pattern overwhelms the eye or leaves too much empty space. Edge work is alike as well — both mixes both hard and soft edges, which affects how smoothly (or abruptly) each pattern merges with real brush, trunks, and rocks. Kuiu Vias's scale index trends a touch higher, making its breakup blocks slightly larger than those in Forloh Deep Cover. Kuiu Vias runs a little denser on our readings, while Forloh Deep Cover leaves slightly more background showing through — which some hunters prefer in simpler, more open environments. Forloh Deep Cover also shows a higher spread index, suggesting it can maintain its breakup across a slightly broader range of shot distances. As always, these results come from flat pattern imagery. Real-world performance depends heavily on terrain, season, and how the garments fit and move.
This is a pattern-only comparison from flat artwork. Terrain, season, and real backgrounds will still push one or the other ahead in specific setups.
Learn how the CamoMatrix AI evaluates camouflage patterns
Defines the dominant size of shapes in the pattern.
Indicates which scale range the pattern leans toward overall.
How busy the pattern is with shapes and noise.
How hard or soft shape boundaries are.