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Found 2 mln. answers for 'with patches of many hues'.

Color-opponent mechanisms for local hue encoding in a hierarchical
Even though most studies agree LGN tunings do not encode unique hues, there exists no such unanimous agree-ment about V1 and V2 tunings. More-over, they found that patches in each cluster have the same local visual eld and that same-hue patches across clusters have a great overlap in...

https://arxiv.org/pdf/1706.10266.pdf

Unsupervised Holistic Image Generation from Key Local Patches
more complicated than conventional image generation tasks as it entails to achieve three goals simultaneously. First, spatial arrangements of input patches need to be inferred since the data does not contain explicit information about the location. To tackle this issue, we assume that in-puts are key...

https://arxiv.org/pdf/1703.10730

Partitioning Patches into Test-equivalence Classes for
Patch generation systems need to consider large spaces of possible modifications in order to address many kinds of defects. One of the key challenges of program These two test-equivalence relations can be composed, thereby producing a more effective (coarse-grained) partitioning of patches into...

https://arxiv.org/pdf/1707.03139

PatchMatch: A Randomized Correspondence Algorithm for Structural...
Most high-level editing approaches meet only one of these criteria. For ex-ample, one family of algorithms known The high synthesis quality of patch optimization methods comes at the expense of more search iterations and angle visualized as hue. (c) 1/4 of the way through the rst iteration...

https://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/patchmatch.pdf

The Generalized PatchMatch
In the most straightforward variant, we associate k nearest neighbors with each patch position. During propagation, we improve the nearest neighbors at the current position by exhaustively testing each of the k nearest neighbors to the left or above (or below or right on even iterations).

https://gfx.cs.princeton.edu/pubs/Barnes_2010_TGP/generalized_pm.pdf

To Survive With Many Patches
While patches are applied to the source tree, the .pc directory is essential for many operations, including taking patches o the stack (quilt pop), and refreshing patches The concept of merging your patches with upstream is identical to applying your patches on a more recent version of the software.

https://users.suse.com/~agruen/quilt.pdf

Gemstones of pakistan
Ruby is more common than corundum ex-hibiting blue or violet hues. Most of the red crys-tals are opaque to translucent, penetrated by nu-merous fractures, and often marred by large patches of calcite (acharacteristic common in the material from Mogolz as well].

https://www.gia.edu/doc/Gemstones-of-Pakistan-Emerald-Ruby-and-Spinel.pdf

The e ffect of b lue f luorescence
Opinions of even the most experienced tradespeople vary. widely. With great conviction, some say that blue fluorescence of different strengths typi Both colorless and colored dia-monds can fluoresce several hues, most commonly blue, yellow, orange, and white.

https://www.gia.edu/doc/WN97.pdf

The PatchMatch Randomized
Many of the most powerful of these methods are patch-based: they divide the image into many small, overlapping rectangles of fixed size (e.g., 7 × 7 On the vertical axis, we count the number of patches with a given distance. Most patches have zero Euclidean distance, indicating perfect coherence.

http://www.connellybarnes.com/work/publications/2011_patchmatch_cacm.pdf

PatchTable: Efcient Patch Queries for Large Datasets and Applications
Modern patch-based methods have delivered high quality results for many applications, by synthesizing or analyzing images in terms of small regions called patches (or neighborhoods). However, these methods have had a difcult time scaling to large quantities of data.

http://www.connellybarnes.com/work/publications/2015_patchtable.pdf