Optimized tile-based texture synthesis

2016

Texture synthesis has obtained a great attention in the areas of Computer vision, human perception and computer graphics. Texture synthesis is the method of developing a large digital image from a small digital image having the same visual appearance of the input image. Synthesizing texture is one of the suitable method to create texture images of arbitrary size. In this Survey discussed about the various work done in the field of texture synthesis.

Texture synthesis is process of constructing large digital texture image from small sample texture without affecting quality of image. Single texture cannot meet the need of variety of texture .This paper Proposed the concept of Multiexemplar Texture Synthesis based on Image Quilting algorithm. Number of traditional techniques such as sample based texture synthesis, multi-scale texture synthesis, exemplar graph texture synthesis, patch based texture synthesis, neighbor searching texture synthesis. To improve the efficiency and quality of image we proposed quilting method and fusing

… of the 14th Eurographics workshop on …, 2003

Composition of mosaics from smaller tile images has a long history and many interesting applications. In the digital age, the opportunities for composing mosaics from many thousands of tile images is achieved through automation. Incorporation of thematic content in mosaics adds an appealing dimension with applications in art, history, education, advertising, and marketing. In this paper, we explore the use of image processing techniques for automated digital composition of mosaics using a relatively small database of tile images with thematic content related to the target image. Existing techniques based on sub-region analysis and mean square error (MSE) criteria for tile assignment are leveraged for composition of a baseline mosaic. In order to meet the challenge of limited datasets, a novel algorithm is proposed that identifies dominant edges in the target image as a basis for a 2nd tier tile assignment process that results in an 'edge priority' mosaic. An optional step is also proposed in which the edge priority mosaic can be further enhanced based on the edge structure in the target image. The final stage enhancement process opens the possibility to further draw out the form and likeness of the subject matter in the target image and also offer an opportunity for incorporating artistic effects in the final rendering. Experimental results show improved rendering with modest size tile databases over baseline MSE mosaics produced using sub-region techniques.

This paper describes a method for synthesizing natural textures that realistically matches given colour texture ap- pearance. The novel texture synthesis method, which we call the roller, is based on the overlapping tiling and subsequent minimum error boundary cut. An optimal double toroidal patch is seamlessly repeated during the syn- thesis step. While the method allows only moderate texture compression it is extremely fast due to separation of the analytical step of the algorithm from the texture synthesis part, universal, and easily implementable in a graph- ical hardware for purpose of real-time colour texture rendering.

Pattern recognition, 2007

Near-regular texture is probably among the most difficult to handle in the texture synthesis area, because the synthesis must preserve the holistic structural property and the local randomness simultaneously. In this paper, motivated by the relationship between a near-regular texture image and an evolutionary system, we propose a novel texture synthesis algorithm. By defining individuals with appropriate attributes and behaviors, we convert the texture synthesis problem to an evolution process of an evolutionary system. It can achieve highquality synthesized results on a large variety of near-regular textures without any extra overhead for memory and pretreatment, and the speed approaches real-time. Moreover, it can be easily generalized to deal with other kinds of textures.

Computer Graphics International, 2004. Proceedings, 2004

Journal of Electrical and Computer Engineering, 2013

Mosaics are images obtained by cementing together small colored fragments and are an ancient example of discrete primitive-based images. Artificial mosaics are illustrations composed by a set of small images called "tiles" that tessellate a source image aiming to reproduce the original visual information in a mosaic-like style. In this paper, we propose a mosaic generation technique based on gradient vector flow (GVF) together with a set of tile cutting heuristics evaluated according to aesthetic criteria. The effectiveness of the proposed approach has been confirmed by a series of tests and comparisons with state-of-the-art techniques.

We propose a fast and adjustable sub-optimal path search algorithm for finding minimum error boundaries between overlapping images. The algorithm may serve as an efficient alternative to traditional slow path search algorithms like the dynamical programming. We use the algorithm in combination with novel adaptive blending to stitch image regions. The technique is then exploited in a framework for sampling-based texture synthesis where the learning phase is clearly separated and the synthesis phase is very simple and fast. The approach exploits the potential of tile-based texturing and produces good and realistic results for a wide range of textures.

Computer Graphics Forum, 2006

We present a wavelet-based approach for selecting patches in patch-based texture synthesis. We randomly select the first block that satisfies a minimum error criterion, computed from the wavelet coefficients (using 1D or 2D wavelets) for the overlapping region. We show that our wavelet-based approach improves texture synthesis for samples where previous work fails, mainly textures with prominent aligned features. Also, it generates similar quality textures when compared against texture synthesis using feature maps with the advantage that our proposed method uses implicit edge information (since it is embedded in the wavelet coefficients) whereas feature maps rely explicitly on edge features. In previous work, the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel-to-pixel difference, disregarding relevant image structures-such as edges-that are relevant in the human visual system and therefore on synthesis of new textures.