The autocorrelation function of an image can be used to detect repetitive patterns of textures. Running each of these nine maps over an image to create a new image of the value of the origin () results in 9 'energy maps,' or conceptually an image with each pixel associated with a vector of 9 texture attributes.Īutocorrelation and Power Spectrum

The resulting 9 maps used by Laws are as follows: L5E5/E5L5 The average of these two measures is the 'edginess' of the content. For instance, L5E5 measures vertical edge content and E5L5 measures horizontal edge content. To these 4, a fifth is sometimes added: W5 = (Wave)įrom Laws' 4 vectors, 16 5x5 'energy maps' are then filtered down to 9 in order to remove certain symmetric pairs. The four vectors and relevant features were as follows: Laws originally used four vectors representing texture features to create sixteen 2D masks from the outer products of the pairs of vectors. Īnother approach is to use local masks to detect various types of texture features. It is used in dentistry for the objective evaluation of lesions, treatment efficacy and bone reconstruction during healing. One negative aspect of the co-occurrence matrix is that the extracted features do not necessarily correspond to visual perception. The edgeness per unit area can be defined by F e d g e n e s s = | th entry in a gray-tone spatial dependence matrix, and Ng is the number of distinct gray-levels in the quantized image. the gradient-based edge detector is applied to this region by producing two outputs for each pixel p: the gradient magnitude Mag(p) and the gradient direction Dir(p). These directions can be represented as an average or in a histogram.Ĭonsider a region with N pixels. After edges have been found the direction of the edges can also be applied as a characteristic of texture and can be useful in determining patterns in the texture. The use of edge detection is to determine the number of edge pixels in a specified region, helps determine a characteristic of texture complexity. In general this approach is easier to compute and is more widely used, since natural textures are made of patterns of irregular subelements. To obtain a structured description a characterization of the spatial relationship of the texels is gathered by using Voronoi tessellation of the texels.Ī statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region. This works well when analyzing artificial textures. To analyze an image texture in computer graphics, there are two ways to approach the issue: Structured Approach and Statistical Approach.Ī structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern. For more accurate segmentation the most useful features are spatial frequency and an average grey level. Image textures are one way that can be used to help in segmentation or classification of images. Image textures can be artificially created or found in natural scenes captured in an image. Image texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image.

Dexture meaning