An evaluation of stochastic models for analysis and synthesis of gray-scale texture

Iversen, Håvard; Lønnestad, Tor

doi:10.1016/0167-8655(94)90018-3

Cited by 18 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We briefly review some recent and representative works and refer the reader to [8] and [12] for more complete surveys. Physical Simulation: It is possible to synthesize certain surface textures by directly simulating their physical generation processes.…”

Section: Previous Workmentioning

confidence: 99%

Fast texture synthesis using tree-structured vector quantization

Wei

Levoy

2000

Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH '00

1,099

865

View full text Add to dashboard Cite

Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given example. Using our algorithm, textures can be generated within seconds, and the synthesized results are always tileable. AbstractTexture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

show abstract

Section: Previous Workmentioning

confidence: 99%

Fast texture synthesis using tree-structured vector quantization

Wei

Levoy

2000

Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH '00

1,099

865

View full text Add to dashboard Cite

show abstract

“…Several texture-synthesis strategies, such as texture-mapping [1,2], statistical texture modeling [3,4] and modeling of texture-formation processes [5,6], have been proposed so far. However, a trade-off that exists between texture-generation speed and quality of the synthesis outcome, makes real-time texture rendering a challenging task, especially, when moderate computing resources are available.…”

Section: Introductionmentioning

confidence: 99%

Fast generation of natural textures with Cellular Neural Networks-based stitching

Ślot

Komatowski

2010

2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA 2010)

View full text Add to dashboard Cite

The following paper presents a novel method for texture synthesis, which combines simple patch-based texture mapping with an appropriate stitching procedure, performed by means of Cellular Neural Networks. Texture mapping involves placement of same-size blocks, extracted randomly from some reference texture image, at regularly-spaced locations. Gaps between blocks are next filled with contents generated by means of a Cellular Neural Network. A CNN is expected to spontaneously transform its initial random state into a texturefitting pattern. The appropriate template is designed by approaching a CNN from a linear filter perspective: template's transfer function is expected to match a spectrum of a target texture. The main advantage of the proposed method is its fast speed of texture rendering, combined with good-quality of generated images.

show abstract

“…As shown in Ref. 2, only those models based on the autocorrelation function of the texture are able to provide a practical synthesis method that can be applied to a wide range of input texture classes. This finding agrees with the fact that first and second order statistics are the most important features to describe purely stochastic textures for the human observer.…”

Section: Introductionmentioning

confidence: 99%

Texture synthesis‐by‐analysis method based on a multiscale early‐vision model

Portilla¹,

Navarro²,

Nestares³

et al. 1996

Opt. Eng

View full text Add to dashboard Cite

A new texture synthesis-by-analysis method, applying a visually based approach that has some important advantages over more traditional texture modeling and synthesis techniques is introduced. The basis of the method is to encode the textural information by sampling both the power spectrum and the histogram of homogeneously textured images. The spectrum is sampled in a log-polar grid using a pyramid Gabor scheme. The input image is split into a set of 16 Gabor channels (using four spatial frequency levels and four orientations), plus a lowpass residual (LPR). The energy and equivalent bandwidths of each channel, as well as the LPR power spectrum and the histogram, are measured and the latter two are compressed. The synthesis process consists of generating 16 Gabor filtered independent noise signals with spectral centers equal to those of the Gabor filters, whose energy and equivalent bandwidths are calculated to reproduce the measured values. These bandpass signals are mixed into a single image, whose LPR power spectrum and histogram are modified to match the original features. Despite the coarse sampling scheme used, very good results have been achieved with nonstructured textures as well as with some quasiperiodic textures. Besides being applicable to a wide range of textures, the method is robust (stable, fully automatic, linear, and with a fixed code length) and compact (it uses only 69 parameters).

show abstract

An evaluation of stochastic models for analysis and synthesis of gray-scale texture

Cited by 18 publications

References 12 publications

Fast texture synthesis using tree-structured vector quantization

Fast texture synthesis using tree-structured vector quantization

Fast generation of natural textures with Cellular Neural Networks-based stitching

Texture synthesis‐by‐analysis method based on a multiscale early‐vision model

Contact Info

Product

Resources

About