Abstract: Recent work has demonstrated the efficacy of Procedural Techniques for simulation of realistic textures emulating rippled-sand and random roughness seafloors, as well as bioturbation by fish feeding pits. Separately, recent work has presented a sonar time series model, which has been shown to agree with theory for the mean, mean square, and spatial coherence of the roughness-scattered acoustic field. In this work, we apply these state of the art environmental generation techniques, inspired by the computer graphics industry, for generation of realistic seafloor textures, combined with the massive parallelization afforded by modern graphics processing units to compute acoustic models, for generation of simulated sonar time series. The resulting time series are then demonstrated to be suitable for coherent synthetic aperture signal processing resulting in a high-fidelity simulated SAS image.

Abstract: A zinc flotation concentrate grade soft measurement method based on a procedural texture characteristic is provided. The method combines expert knowledge and a data modeling process. That a foam imageis represented by a single-frame texture characteristic based on image statistical characteristics is put forward according to key points of foam observation by on-site workers; digitalization of thecurrent production state with a texture sequence is put forward according to a characteristic that the current production state is determined by observation of the foam state in a period of time by on-site workers; and a modeling method for the texture sequence is provided to reduce the dimensionality of a feature vector. An improved boosting decision tree algorithm is adopted in a prediction algorithm to effectively inhibit an overfitting problem caused by too-fast learning, and improving generalization capability. Experiments prove that the method is simple in calculation, high in implementation speed and high in prediction accuracy, actual operation on site is convenient so as to direct spot operation in real time to optimize a production process, and a problem that on-line detection of the grade of zinc ore is difficult is solved.

Abstract: Semantic attributes are commonly used for texture description. They can be used to describe the information of a texture, such as patterns, textons, distributions, brightness, and so on. Generally speaking, semantic attributes are more concrete descriptors than perceptual features. Therefore, it is practical to generate texture images from semantic attributes. In this paper, we propose to generate high-quality texture images from semantic attributes. Over the last two decades, several works have been done on texture synthesis and generation. Most of them focusing on example-based texture synthesis and procedural texture generation. Semantic attributes based texture generation still deserves more devotion. Gan et al. proposed a useful joint model for perception driven texture generation. However, perceptual features are nonobjective spatial statistics used by humans to distinguish different textures in pre-attentive situations. To give more describing information about texture appearance, semantic attributes which are more in line with human description habits are desired. In this paper, we use sigmoid cross entropy loss in an auxiliary model to provide enough information for a generator. Consequently, the discriminator is released from the relatively intractable mission of figuring out the joint distribution of condition vectors and samples. To demonstrate the validity of our method, we compare our method to Gan et al.'s method on generating textures by designing experiments on PTD and DTD. All experimental results show that our model can generate textures from semantic attributes.

Abstract: Humans naturally use semantic descriptions to express their visual perception of textures; this is also the fact for perception and description of wallpaper texture. Classification of wallpaper's style is mainly based on understanding of visual information. However, the complexity of real-world wallpaper images is difficult to be captured by existing datasets. Inspired by a publicly available Procedural Textures Dataset, a number of wallpaper images was collected and assembled into a wallpaper dataset. A series of psychophysical experiments was performed to further collect semantic descriptions for this dataset. Each wallpaper was labeled with 5–10 semantic descriptions. More importantly, our dataset contains complex wallpaper images with rich annotations. To our best knowledge, our dataset is the first public wallpaper dataset with semantic descriptions. We use label distribution to analysis semantic descriptions and texture characteristics. Furthermore, a texture generation method based on GAN was tested using our wallpaper dataset, which produced state-of-the-art results.

Abstract: We present an efficient system for synthesizing textures over fluid surfaces in a solid texturing context. The technique is simple and intuitive for artists using modern, commercially available fluid simulators. Instead of working with 2D surface maps like other fluid texture synthesis approaches, we advect 3D reference space transforms with the fluid simulation. The reference transforms are then projected onto the final surface mesh with a radius of influence control, and used for solid texturing lookup. Ray intersections of the fluid surface interpolate any transforms with overlapping control radii to determine the reference lookup of the solid texture. The final texture exhibits excellent spatial and temporal coherence with none of the artifacts that plagued previous map based approaches.

Abstract: Procedural noise functions have many applications in computer graphics, ranging from texture synthesis to atmospheric effect simulation or to landscape geometry specification. Noise can either be precomputed and stored into a texture, or evaluated directly at application runtime. This choice offers a tradeoff between image variance, memory consumption and performance. Advanced tiling algorithms can be used to decrease visual repetition. Wang tiles allow a plane to be tiled in a non-periodic way, using a relatively small set of textures. Tiles can be arranged in a single texture map to enable the GPU to use hardware filtering. In this paper, we present modifications to several popular procedural noise functions that directly produce texture maps containing the smallest complete Wang tile set. The findings presented in this paper enable non-periodic tiling of these noise functions and textures based on them, both at runtime and as a preprocessing step. These findings also allow decreasing repetition of noise-based effects in computer-generated images at a small performance cost, while maintaining or even reducing the memory consumption.

Abstract: We propose a new by-example noise algorithm that takes as input a small example of a stochastic texture and synthesizes an infinite output with the same appearance. It works on any kind of random-phase inputs as well as on many non-random-phase inputs that are stochastic and non-periodic, typically natural textures such as moss, granite, sand, bark, etc. Our algorithm achieves high-quality results comparable to state-of-the-art procedural-noise techniques but is more than 20 times faster. Our approach is conceptually simple: we partition the output texture space on a triangle grid and associate each vertex with a random patch from the input such that the evaluation inside a triangle is done by blending 3 patches. The key to this approach is the blending operation that usually produces visual artifacts such as ghosting, softened discontinuities and reduced contrast, or introduces new colors not present in the input. We analyze these problems by showing how linear blending impacts the histogram and show that a blending operator that preserves the histogram prevents these problems. The main requirement for a rendering application is to implement such an operator in a fragment shader without further post-processing, i.e. we need a histogram-preserving blending operator that operates only at the pixel level. Our insight for the design of this operator is that, with Gaussian inputs, histogram-preserving blending boils down to mean and variance preservation, which is simple to obtain analytically. We extend this idea to non-Gaussian inputs by "Gaussianizing" them with a histogram transformation and "de-Gaussianizing" them with the inverse transformation after the blending operation. We show how to precompute and store these histogram transformations such that our algorithm can be implemented in a fragment shader.