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Image Quilting for Texture Synthesis ..

Texture synthesis can also produce tileable images by properly handling the boundary conditions.

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Project 3: Image Quilting for Texture Synthesis and Transfer

1. Division of the highest order, bar 56: Location of the movement'ssingle wave climax; the only in the movement, the loudest dynamic;sustaining climactic tone, a high Eb; completion of the tonal climax ofthe subject entry fifth sequence, A to Eb; end of the use of the subjectin proper form until S+Si union; begin use of the subject inversion;termination of crescendo and expanding textural density; termination ofpercussion; begin tonal, textural, and dynamic descent.

 Figure 1. Synthesizability (in [0, 1]) of texture examples detected by our method.

Figure 6. Synthesizability scores of texture examples and the `best' synthesized textures by texture synthesis methods. Top: exemplar; bottom: synthesized.

Image Quilting for Texture Synthesis and Transfer

The synthesis of textures is one of the better ways to createtexture images of arbitrary size.

We can augment the texture synthesis approach above to get a texture transfer algorithm. That is re-rendering an image with the texture samples of a different image. Each sample patch that we add to our synthesized image must now respect two different constraints: (a) it should have some agreement with the already synthesized parts (this is the constraint we used in texture synthesis), and (b) it should have some correspondance with the image we want re-render. We will use a parameter α to determine the tradeoff between these two constraints. To come up with a term for part (b) we need some measurement of how much a patch agrees with the underlying image. We can do this by calculating the SSD of a patch and the image on some corresponding quantity. One such quantity could be image intensity or the blurred image intensity.

The paper suggest to run multiple iterations of this while decreasing the tile size and adjusting α each time to get the best results. If we run multiple iterations we will need to incorporate the agreement of a patch with the already synthesized image and not just with the overlap region. So the error term will end up being something like this

Since reproducing the visual realism of the real world is a major goal for computer graphics, textures are commonly employed when rendering synthetic images.

Choosing Images for Texture Synthesis - Corel Discovery …

Given a texture sample in the form of an image, we create asimilar texture over an irregular mesh hierarchy that has been placed on agiven surface.

In contrast to other techniques, the size of the patch is not chosen a-priori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture.

We build up a new texture dataset , which contains 21,302 texture samples, and their synthesized results by four standard methods. All textures of the dataset are annotated according to their synthesizability: good, acceptable, and bad. The best synthesis method for each texture sample is also recorded. See Fig.2 for examples of such annotation.

Figure 7. The most synthesizable region as detected. The synthesizability of the whole images (IS) vs. the selected region (RS) are given.
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  • Graphcut Textures: Image and Video Synthesis Using …

    We specifically explore it in 2D and 3D to perform video texture synthesis in additionto regular image synthesis.

  • ImageQuilting - Image Quilting for Texture Synthesis and Transfer

    Dengxin Dai, Hayko Riemenschneider, and Luc Van Gool.. The Synthesizability of Texture Examples. In .

  • Project 3: Image Quilting for Texture Synthesis and …

    For some applications in image processing, it is desirable to have a certain texture fill an arbitrary amount of space

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Texture Synthesis by Image Quilting

The baseline will work reasonably well for many images, but for certain types of images there are very noticable failures. In particular humans are very sensitive to distortions in faces and distortions in straight lines. Images with large areas of high-frequency texture also tend to behave oddly with simple energy functions.

Geometry texture synthesis based on Laplacian texture image

Figure 2. Three texture examples from our dataset with their annotations of synthesizability. Left: texture exemplars; right: synthesized textures.

texture synthesis free download - SourceForge

Include results for these images and at least three images from an outside source for both algorithms. You are welcome to shrink them or crop them as you see fit, as long as you keep the general texture.

The Synthesizability of Texture Examples - ETH Z

Instruments rapidly drop out after the climax and the texture and dynamicsthin. A stretto ensues at bar 65 with entries cascading rapidly down throughthe strings in the cycle of fifths. At 78 the counterpoint converges onA again. In this measure the complete original upright version of the subjectis heard, played simultaneously with its complete inversion. This is thesolitary complete union of the whole subject, S, and its inversion,Si, in the entire movement (designated "S+Siunion" in figure 20). The celeste enters and plays undulating arpeggios,weaving around the subject and its inversion as the subject unites withits inversion. The two complementary forms begin together on A, climaxtogether on Eb and end together on A, all the while accompanied by an Ebpedal, thus echoing the tonal structure of the subject and the entire movement.

The Synthesizability of Texture Examples

Example-based texture synthesis (ETS) has been widely used to generate high quality textures of desired sizes from a small example. However, not all textures are equally well reproducible that way. We predict how synthesizable a particular texture is by ETS. We introduce a dataset (21, 302 textures) of which all images have been annotated in terms of their synthesizability. We design a set of texture features, such as ‘textureness’, homogeneity, repetitiveness, and irregularity, and train a predictor using these features on the data collection. This work is the first attempt to quantify this image property, and we find that texture synthesizability can be learned and predicted. We use this insight to trim images to parts that are more synthesizable. Also we suggest which texture synthesis method is best suited to synthesise a given texture. Our approach can be seen as ‘winner-uses-all’: picking one method among several alternatives, ending up with an overall superior ETS method. Such strategy could also be considered for other vision tasks: rather than building an even stronger method, choose from existing methods based on some simple preprocessing.

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