We present texture cache approximation as a method for using existing hardware on GPUs to eliminate costly global memory accesses. We develop a technique for using a GPU’s texture fetch units to generate approximate values, and argue that this technique is applicable to a wide variety of GPU kernels. Applying texture cache approximation to an image blur kernel on an NVIDIA 780GTX, we obtain a 1...

Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general pro...

In this paper we present a non-causal non-parametric multiscale Markov random field (MRF) texture model that is capable of synthesising a wide variety of textures. The textures that this model is capable of synthesising vary from the highly structured to the stochastic type and include those found in the Brodatz album of textures. The texture model uses Parzen estimation to estimate the conditi...

Texture analysis and synthesis is very important for computer graphics, vision, and image processing. This paper describes an algorithm which can produce new textures with a matching visual appearance from a given example image. Our algorithm is based on a model that characterizes textures using a nonlinear deterministic function. During analysis, an example texture is summarized into this func...

Texture is an important characteristic di erentiating objects or regions of interest in an image. A variety of approaches to texture analysis has previously been proposed; the approach in this paper is from the stochastic eld, whereby a two-dimensional Volterra model is used to represent a texture eld. Statistical moments are introduced as a means of determining the Volterra model generator for...

We present a nonparametric Markov Random Field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this...

In this paper, neural networks are applied on high resolution IKONOS images for road detection. It was tried to optimize neural network's functionality using a variety of texture parameters with different window sizes and gray level numbers. Both the source image and pre-classified image were used for texture parameter extraction. The obtained results were compared in terms of road and backgrou...

Partition of unity parametrics (PUPs) are a recent framework designed for geometric modeling. We propose employing PUPs for procedural texture synthesis, taking advantage of the framework’s guarantees of high continuity and local support. Using PUPs to interpolate among data values distributed through the plane, the problem of texture synthesis can be approached from the perspective of point pl...

Textures have a variety of applications in computer graphics. While the original idea of texture mapping was just to cover the surface of an object with an image to avoid having to model every detail geometrically, in time a lot of other uses have been proposed. In this paper, the most important applications of texture mapping (except the original approach) will be explained.

A perspective view of a slanted textured surface shows systematic changes in the density, area, and aspect-ratio of texture elements. These apparent changes in texture element properties can be analyzed to recover information about the physical layout of the scene. However, in practice it is difficult to identify texture elements, especially in images where the texture elements are partially oc...