Salient structural elements based texture synthesis

In the research of virtual hepatic surgery, the synthesis of hepatic texture space and the texture mapping of the hepatic body model are inseparable. Different methods for hepatic volume texture on the authenticity and mapping speed tend to have much difference. Based on computer unified device architecture (CUDA), this paper tries to present a texture synthesis and mapping method for the accelerating of hepatic body, which is used to solve the problem that traditional method under the hepatic volumetric texture synthesis and mapping process cost too much time. This new method may eliminate the traditional serial correlation of texture synthesis. What’s more, it may improve the efficiency of texture synthesis when the multithreading parallel processing for synthesis in the hepatic volumetric texture space in selecting and distributing blocks based on CUDA architecture.

In this paper we introduce a new neighborhood system based Nonparametric Markov Random Field (NMRF) model for texture synthesis. The objective of this work is to generate minimal neighborhood system, in MRF based texture synthesis algorithms. Compared to the earlier neighborhood system (circular), the proposed one is not isometric and has fewer number of pixels within the neighborhood.This reduction in the number of neighborhood pixels does not degrade the synthesis results compared to the existing neighborhood systems.Two popular texture synthesis algorithms (based on nonparametric MRF) have been tested with the new neighborhood system for two standard texture databases.The results establish the efficacy of the proposed neighborhood system in terms of computational gain, with no visible degradation compared to the earlier systems.

Discriminating contrast discontinuities: asymmetries, dipper functions, and perceptual learning

Summary form only given, as follows. A cluster-based probability model has been found to perform extremely well at capturing the complex structures in natural textures (e.g., better than Markov random field models). Its success is mainly due to its ability to handle high dimensionality, via large conditioning neighborhoods over multiple scales, and to generalize salient characteristics from limited training data. Imposing a tree structure on this model provides not only the benefit of reducing computational complexity, but also a new benefit, the trees are mutable, allowing us to mix and match models for different sources. This flexibility is of increasing importance in emerging applications such as database retrieval for sound, image and video.

The problem of digital painting is considered from a signal processing viewpoint, and is reconsidered as a problem of directed texture synthesis. It is an important characteristic of natural texture that detail may be evident at many scales, and the detail at each scale may have distinct characteristics. A “sparse convolution” procedure for generating random textures with arbitrary spectral content is described. The capability of specifying the texture spectrum (and thus the amount of detail at each scale) is an improvement over stochastic texture synthesis processes which are scalebound or which have a prescribed 1/f spectrum. This spectral texture synthesis procedure provides the basis for a digital paint system which rivals the textural sophistication of traditional artistic media. Applications in terrain synthesis and texturing computer-rendered objects are also shown.

In thiswork, a newalgorithmis proposed for fast estimation of nonparametricmultivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data space.The goal of the proposed algorithm is to use the finite support property of kernels for fast estimation of density. Compared to earlier approaches, this work explains the need of using boundaries (for partitioning the space) instead of centroids (used in earlier approaches), for better unsupervised nature (less user incorporation), and lesser (or atleast same) computational complexity. In earlier approaches, the finite support of a fixed kernel varies within the space due to the use of cluster centroids. It has been argued that if one uses boundaries (for partitioning) rather than centroids, the finite support of a fixed kernel does not change for a constant precision error. This fact introduces better unsupervision within the estimation framework. Themain contributionof thiswork is the insight gained in the kernel density estimation with the incorporation of clustering algortihm and its application in texture synthesis. Texture synthesis through nonparametric, noncausal, Markov random field (MRF), has been implemented earlier through estimation of and sampling from nonparametric conditional density. The incorporation of the proposed kernel density estimation algorithm within the earlier texture synthesis algorithm reduces the computational complexity with perceptually same results. These results provide the efficacy of the proposed algorithm within the context of natural texture synthesis.

This work presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and de-saturation is performed in the u'v'Y color space, to change the saturation value in the chromaticity diagram, and adaptive histogram equalization then is adopted to adjust the luminance component. Additionally, this work presents a new patching method using the Markov Random Field (MRF) model of texture synthesis. Eliminating undesirable aged painting patterns, such as stains, crevices, and artifacts, and then filling the lacuna regions with the appropriate textures is simple and efficient. The synthesization procedure integrates three key approaches, weighted mask, annular scan and auxiliary, with neighborhood searching. These approaches can maintain a complete shape and prevent edge disconnection in the final results. Moreover, the boundary between original and synthesized paintings is seamless, and unable to distinguish in which the undesirable pattern appears.

Texture synthesis has been an active research area in computer graphics, vision, and image processing. A hybrid texture synthesis approach that combines the strength of Ashikhmin’s [1] and Liang’s [11] algorithms is presented in this paper. Using patches from the input sample texture to extend the input itself, more suitable valid candidate pixels in the input sample can be more precisely determined. An extra original position array is used to record the valid pixel position in the extended region of input sample texture such that the candidate pixel can be retrieved efficiently. Pasting patches with more global information of texture features to initialize the output texture, the texture synthesis process tends to grow larger pieces in the output texture so that the perceptual similarity is reserved as much as possible. Many classes of textures have been used to test our approach. As the experimental results showed, the proposed hybrid approach effectively and efficiently synthesizes high-quality textures from a wide variety of textures.

Analytical approaches, based on digitised 2D texture models, for an automatic solid (3D) texture synthesis have been recently introduced to Computer Graphics. However, these approaches cannot provide satisfactory solutions in the usual case of natural anisotropic textures (wood grain for example). Indeed, solid texture synthesis requires particular care, and sometimes external knowledge to "guess" the internal structure of solid textures because only 2D texture models are used for analysis. By making some basic assumptions about the internal structure of solid textures, we propose a very efficient method based on a hybrid analysis (spectral and histogram) for an automatic synthesis of solid textures. This new method allows us to obtain high precision solid textures (closely resembling initial models) in a large number of cases, including the difficult case of anisotropic textures.

A new gray level morphological gradient method is proposed which uses gradient projections to both provide orientation information and also to reduce sensitivity to added noise. This results in a substantial performance improvement for both the blur minimum operator (BMO) and the well known morphological gradient defined as the difference between a dilated and eroded image using a symmetric structuring element. This new method is particularly attractive for applications which use morphological filtering at higher levels of processing, since complex structuring elements may often be decomposed into smaller structuring elements suitable for gradient operations. The performance is analyzed with respect to edge segment orientation accuracy using an image content model of an ideal sharp edge blurred by a square or circular aperture function with no preferential relationship between the orientation and displacement of the edge and the pixel grid. Sensitivity to noise can be evaluated in terms of the probability of false edge detection for a variety of noise sources. The theoretical results are supported with simulations designed to demonstrate performance expectations.

The large-scale matter distribution represents a complex network of structure elements such as voids, clusters, filaments, and sheets. This network is spanned by a point distribution. The global properties of the point process can be measured by different statistical methods, which, however, do not describe directly the structure elements. The morphology of structure elements is an important property of the point distribution. Here we apply the core-sampling method to various Voronoi tessellations. Using the core-sampling method we identify one- and two-dimensional structure elements (filaments and sheets) in these Voronoi tessellations and reconstruct their mean separation along random straight lines. We compare the results of the core-sampling method with the a priori known structure elements of the Voronoi tessellations under consideration and find good agreement between the expected and found structure parameters, even in the presence of substantial noise. We conclude that the core-sampling method is a potentially powerful tool to investigate the distribution of such structure elements like filaments and walls of galaxies.

This paper presents a novel texture synthesis algorithm that performs a sparse expansion of the patches of the image in a dictionary learned from an input exemplar. The synthesized texture is computed through the minimization of a non-convex energy that takes into account several constraints. Our first contribution is the computation of a sparse expansion of the patches imposing that the dictionary atoms are used in the same proportions as in the exemplar. This is crucial to enable a fair representation of the features of the input image during the synthesis process. Our second contribution is the use of additional penalty terms in the variational formulation to maintain the histogram and the low frequency content of the input. Lastly we introduce a non-linear reconstruction process that stitches together patches without introducing blur. Numerical results illustrate the importance of each of these contributions to achieve state of the art texture synthesis.

Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.

This paper proposes a general solution for point set matching that effectively addresses the challenges of low-overlap, sparse, or featureless point set matching (LSFPM). Unlike previous methods that mainly rely on feature or neighborhood similarity that often fail under such difficult conditions, this work proposes a Geometry-based Point Matching (GPM) method. GPM first introduces two geometric concepts: the “Structural Element” (SE) and the “Superstructural Element” (SSE), both of which are constructed based on local geometric structures. The SSE is an enhanced version of the SE. A descriptor for the SE, called the SE Descriptor (SED), is designed to encode the SE and facilitate an efficient geometry-based similarity metric. We demonstrate that the cosine similarity of SEDs is invariant to scale and rotation. Subsequently, a SE Matching Maximization (SEMM) problem is formulated to identify a size-penalized SSE set that maximizes the sum of similarities. This problem is efficiently solved using the proposed SEMM-MCMC (Markov Chain Monte Carlo) algorithm. The matched SSEs then vote on corresponding point matches, generating high-confidence one-to-one matches, low-confidence one-to-one matches and one-to-many matches. Finally, the InferMatch algorithm is proposed to jointly assess low-confidence one-to-one point set matching while simultaneously distinguishing one-to-many point set matching. The GPM approach can also complement other feature-based and motion-based methods. It has been extensively validated on both synthetic and real datasets, demonstrating its versatility in addressing various point set matching problems, and is not limited to the LSFPM problem. Extensive experiments on diverse datasets, including SPair-71k, UAVDT, VisDrone2021-MOT, and SparseMatch, further demonstrate the robustness and versatility of GPM. The proposed approach significantly improves matching performance under challenging conditions and effectively addresses key limitations of existing point set matching methods.

This paper presents an example based non-photorealistic image rendering algorithm, which employs patch-based texture synthesis approaches, as well as speeds up the texture synthesis process by exploiting particle swarm optimization during the matching search process. This algorithm also accomplishes orientation control of matching patches in structure and details through appropriate parameter setting. Once a certain sample image with artistic style is presented, the algorithm can accomplish image analogy by transferring the style to the target.