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Image Texture
An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image. Image texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in segmentation or classification of images. For more accurate segmentation the most useful features are spatial frequency and an average grey level. To analyze an image texture in computer graphics, there are two ways to approach the issue: Structured Approach and Statistical Approach. Structured Approach A structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern. This works well when analyzing artificial textures. To obtain a structured description a characterization of the spatial relationship of the texels is gathered by us ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linda Shapiro
Linda G. Shapiro is a professor in the Department of Computer Science and Engineering, a Professor of Electrical Engineering, and Adjunct Professor of Biomedical Informatics and Medical Education at the University of Washington. Education and Experience Shapiro graduated with a B.S. with highest distinction in Mathematics and Computer Science from the University of Illinois in 1970. She completed her M.S. in Computer Science from University of Iowa in 1972 and her Ph.D. in Computer Science from University of Iowa in 1974. She was a faculty member in Computer Science at Kansas State University from 1974 to 1978 and at Virginia Polytechnic Institute and State University from 1979 to 1984. She then spent two years as Director of Intelligent Systems at Machine Vision International in Ann Arbor, Michigan. She has been an IEEE Fellow since 1995, an IAPR fellow since 2000, and has been editor-in-chief of ''CVGIP: Image Understanding''. Professor Shapiro received the Pattern Recogniti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Segmentation (image Processing)
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects ( sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279–325, New Jersey, Prentice-Hall, Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification Of Images
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Texel (graphics)
In computer graphics, a texel, texture element, or texture pixel is the fundamental unit of a texture map. Textures are represented by arrays of texels representing the texture space, just as other images are represented by arrays of pixels. Texels can also be described by image regions that are obtained through simple procedures such as thresholding. Voronoi tesselation can be used to define their spatial relationships—divisions are made at the midpoints between the centroids of each texel and the centroids of every surrounding texel for the entire texture. This results in each texel centroid having a Voronoi polygon surrounding it, which consists of all points that are closer to its own texel centroid than any other centroid. Rendering When texturing a 3D surface or surfaces (a process known as texture mapping), the renderer maps texels to appropriate pixels in the geometric fragment (typically a triangle) in the output picture. On modern computers, this operatio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Voronoi Tessellation
{{Disambiguation, surname ...
Voronoi or Voronoy is a Slavic masculine surname; its feminine counterpart is Voronaya. It may refer to *Georgy Voronoy (1868–1908), Russian and Ukrainian mathematician **Voronoi diagram **Weighted Voronoi diagram ** Voronoi deformation density **Voronoi formula **Voronoi pole **Centroidal Voronoi tessellation In geometry, a centroidal Voronoi tessellation (CVT) is a special type of Voronoi tessellation in which the generating point of each Voronoi cell is also its centroid (center of mass). It can be viewed as an optimal partition corresponding to an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Edge Detection
Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as '' step detection'' and the problem of finding signal discontinuities over time is known as '' change detection''. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. Motivations The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. It can be shown that under rather general assumptions for an image formation model, discontinuities in image brightness are likely to correspond to: * discontinuities in depth, * discontinuities in surface orientation, * changes in material properties and * variations in scene ill ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Co-occurrence Matrix
A co-occurrence matrix or co-occurrence distribution (also referred to as : ''gray-level co-occurrence matrices'' GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in medical image analysis. Method Given a grey-level image I, co-occurrence matrix computes how often pairs of pixels with a specific value and offset occur in the image. * The offset, (\Delta x, \Delta y), is a position operator that can be applied to any pixel in the image (ignoring edge effects): for instance, (1, 2) could indicate "one down, two right". * An image with p different pixel values will produce a p \times p co-occurrence matrix, for the given offset. * The (i, j)^\text value of the co-occurrence matrix gives the number of times in the image that the i^\text and j^\text pixel values occur in the relation given by the offset ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gabor Filter
In image processing, a Gabor filter, named after Dennis Gabor, is a linear filter used for texture analysis, which essentially means that it analyzes whether there is any specific frequency content in the image in specific directions in a localized region around the point or region of analysis. Frequency and orientation representations of Gabor filters are claimed by many contemporary vision scientists to be similar to those of the human visual system. They have been found to be particularly appropriate for texture representation and discrimination. In the spatial domain, a 2-D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave (see Gabor transform). Some authors claim that simple cells in the visual cortex of mammalian brains can be modeled by Gabor functions. Thus, image analysis with Gabor filters is thought by some to be similar to perception in the human visual system. Definition Its impulse response is defined by a sinusoidal wave (a plan ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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