Filling Algorithms Synthesizing One Pixel Really Synthesizing One Pixel The pixel metric doesn’t matter Dynamic Programming solution

Filling Algorithms Synthesizing One Pixel Really Synthesizing One Pixel The pixel metric doesn’t matter Dynamic Programming solution www.phwiki.com

Filling Algorithms Synthesizing One Pixel Really Synthesizing One Pixel The pixel metric doesn’t matter Dynamic Programming solution

Dunn, Scott, Contributing Editor has reference to this Academic Journal, PHwiki organized this Journal Filling Algorithms Pixelwise MRFs Chaos Mosaics Patch segments are pasted, overlapping, across the image. Then either: Ambiguities are removed by smoothing (Chaos Mosaics-MSR). Or a least cost path through the (chosen) overlapping images are found. Efros’01 uses dynamic programming, while Graphcut textures’03 uses graphcut. Pixel distributions are determined by comparision with those with similar neighbourhoods. These distributions are sampled from or heuristics are per as long as med on them to determine how to fill them. Synthesizing One Pixel Assuming Markov property, what is conditional probability distribution of p, given the neighbourhood window Instead of constructing a model, let’s directly search the input image as long as all such neighbourhoods to produce a histogram as long as p To synthesize p, just pick one match at r in addition to om Infinite sample image Generated image SAMPLE p Taken from Efros’ original presentation Really Synthesizing One Pixel finite sample image Generated image p However, since our sample image is finite, an exact neighbourhood match might not be present So we find the best match using SSD error (weighted by a Gaussian to emphasize local structure), in addition to take all samples within some distance from that match SAMPLE Taken from Efros’ original presentation

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The pixel metric doesn’t matter One of the surprising things about this is that the choice pixel metric doesn’t seem to matter. Efros uses . 2 2 on RGB space, I’ve been using . 1, while Criminisi uses a metric based on the CIELab colour space. It doesn’t seem to matter which you use, presumably because we are only concerned with nearest neighbours, in addition to they are all topologically equivalent. Dynamic Programming solution block Chaotic Mosaics Graphcuts solution Chaotic Mosaics Takes full advantage of the power of graphcuts method, it treats the whole image as one patch in addition to finds optimal joins along it. Pros: Finds optimal ( in addition to often seamless) matches Cons: Doesn’t find anything else, the recycling the optimal matches still leaves you with tiling artefacts.

Graphcuts solution Chaotic Mosaics Image Quilting Graphcuts Onion skin or Outside in The first. The simplest Works with single textures or simple convex filling regions Just picks away at the image one layer at a time Pixel choice in addition to Filling Algorithms Linear structure propagation Onion skin +pushing in on linear textures Better than Onion skin as long as multi textural environments When all you have is hammer, everything starts to look like a nail. ~ Artefacts from trying too hard. Pixel choice in addition to Filling Algorithms Missing Data Correction in Still Images in addition to Image Sequences, Bornard et al. 2002

Linear structure propagation Onion skin +pushing in on linear textures Better than Onion skin as long as multi textural environments When all you have is hammer, everything starts to look like a nail. ~ Artefacts from trying too hard. Pixel choice in addition to Filling Algorithms Missing Data Correction in Still Images in addition to Image Sequences, Bornard et al. 2002 Filling Algorithms Onion Peel Vs. Linear propagation Push In Now Onion Peel Pixel choice in addition to Filling Algorithms Max. entropy fill Consistent with MRF assumptions. Locally convex with a minimum of occlusions at point of fill. Spirals in on simple shapes.

Why Coarse to Fine St in addition to ard Efros 15 pixels St in addition to ard efros 21 pixels New metric Efros 15 pixels Efros uni as long as m pixel weighting 15 C2f uni as long as m pixel weighting Course to fine 15 pixel nhood The same but quicker Structures Why Coarse to Fine Structures As Textures Why Coarse to Fine

Structures As Textures Why Coarse to Fine Texture as structure Strong linear propagation comes from efros style fills naturally. Why Coarse to Fine Not readily apparent due to the onion skin fill Efros Linear propagation The Efros Algorithm with my pixel choice

No guarantee it’s any better than linear propagation The algorithm often spots at the coarser levels that it has insufficient data to complete No guarantee it’s any better than linear propagation Annoyingly, this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the higher levels is still needed. No guarantee it’s any better than linear propagation Annoyingly, this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the higher levels is still needed.

No guarantee it’s any better than linear propagation Annoyingly, this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the lowest resolution is still needed. No guarantee it’s any better than linear propagation Annoyingly, this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the lowest resolution is still needed. No guarantee it’s any better than linear propagation Annoyingly this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the lowest resolution is still needed.

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No guarantee it’s any better than linear propagation Annoyingly, this problem is not solvable by more data, in the sense of higher resolution images. In as long as mation about how to propagate edges at the lowest resolution is still needed. No guarantee it’s any better than linear propagation Compare it with a smaller fill Is manifold learning possible Up to a point, we don’t care what colour the books are when propagating the shelf. Similar structural edge patterns are apparent everywhere. Why Coarse to Fine

Why Coarse to Fine Problems Speed Massively slower(hours rather than seconds) than patch based synthesis even with coarse to fine reducing neighbourhood size. Can we reduce the search space via image segmentation Alternatively turn our soft coarse to fine constraints into something harder, by only testing pixels from the neighbourhoods of the k-closest fits at a coarser level. Problems Surprisingly, this could even increase robustness by preventing the growth of miss-fittings

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Dunn, Scott is from United States and they belong to PC World and they are from  San Francisco, United States got related to this Particular Journal. and Dunn, Scott deal with the subjects like Computer Hardware; Computers; End-Users; PCs/Macs/Laptops

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