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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 offse ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Matrix (mathematics)
In mathematics, a matrix (plural matrices) is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns, which is used to represent a mathematical object or a property of such an object. For example, \begin1 & 9 & -13 \\20 & 5 & -6 \end is a matrix with two rows and three columns. This is often referred to as a "two by three matrix", a "-matrix", or a matrix of dimension . Without further specifications, matrices represent linear maps, and allow explicit computations in linear algebra. Therefore, the study of matrices is a large part of linear algebra, and most properties and operations of abstract linear algebra can be expressed in terms of matrices. For example, matrix multiplication represents composition of linear maps. Not all matrices are related to linear algebra. This is, in particular, the case in graph theory, of incidence matrices, and adjacency matrices. ''This article focuses on matrices related to linear algebra, and, unle ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Digital Image
A digital image is an image composed of picture elements, also known as ''pixels'', each with ''finite'', '' discrete quantities'' of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with ''x'', ''y'' on the x-axis and y-axis, respectively. Depending on whether the image resolution is fixed, it may be of vector or raster type. Raster Raster images have a finite set of digital values, called ''picture elements'' or pixels. The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding antiquated values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Grayscale
In digital photography, computer-generated imagery, and colorimetry, a grayscale image is one in which the value of each pixel is a single sample representing only an ''amount'' of light; that is, it carries only intensity information. Grayscale images, a kind of black-and-white or gray monochrome, are composed exclusively of shades of gray. The contrast ranges from black at the weakest intensity to white at the strongest. Grayscale images are distinct from one-bit bi-tonal black-and-white images, which, in the context of computer imaging, are images with only two colors: black and white (also called ''bilevel'' or '' binary images''). Grayscale images have many shades of gray in between. Grayscale images can be the result of measuring the intensity of light at each pixel according to a particular weighted combination of frequencies (or wavelengths), and in such cases they are monochromatic proper when only a single frequency (in practice, a narrow band of frequencies) is ca ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pixel
In digital imaging, a pixel (abbreviated px), pel, or picture element is the smallest addressable element in a raster image, or the smallest point in an all points addressable display device. In most digital display devices, pixels are the smallest element that can be manipulated through software. Each pixel is a sample of an original image; more samples typically provide more accurate representations of the original. The intensity of each pixel is variable. In color imaging systems, a color is typically represented by three or four component intensities such as red, green, and blue, or cyan, magenta, yellow, and black. In some contexts (such as descriptions of camera sensors), ''pixel'' refers to a single scalar element of a multi-component representation (called a ''photosite'' in the camera sensor context, although ''sensel'' is sometimes used), while in yet other contexts (like MRI) it may refer to a set of component intensities for a spatial position. Etymology The w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Numeral System
A binary number is a number expressed in the base-2 numeral system or binary numeral system, a method of mathematical expression which uses only two symbols: typically "0" (zero) and "1" ( one). The base-2 numeral system is a positional notation with a radix of 2. Each digit is referred to as a bit, or binary digit. Because of its straightforward implementation in digital electronic circuitry using logic gates, the binary system is used by almost all modern computers and computer-based devices, as a preferred system of use, over various other human techniques of communication, because of the simplicity of the language and the noise immunity in physical implementation. History The modern binary number system was studied in Europe in the 16th and 17th centuries by Thomas Harriot, Juan Caramuel y Lobkowitz, and Gottfried Leibniz. However, systems related to binary numbers have appeared earlier in multiple cultures including ancient Egypt, China, and India. Leibniz was specifica ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Texture (computer Graphics)
Texture mapping is a method for mapping a texture on a computer-generated graphic. Texture here can be high frequency detail, surface texture, or color. History The original technique was pioneered by Edwin Catmull in 1974. Texture mapping originally referred to diffuse mapping, a method that simply mapped pixels from a texture to a 3D surface ("wrapping" the image around the object). In recent decades, the advent of multi-pass rendering, multitexturing, mipmaps, and more complex mappings such as height mapping, bump mapping, normal mapping, displacement mapping, reflection mapping, specular mapping, occlusion mapping, and many other variations on the technique (controlled by a materials system) have made it possible to simulate near-photorealism in real time by vastly reducing the number of polygons and lighting calculations needed to construct a realistic and functional 3D scene. Texture maps A is an image applied (mapped) to the surface of a shape or polygon. This ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Color Mapping
Color mapping is a function that maps (transforms) the colors of one (source) image to the colors of another (target) image. A color mapping may be referred to as the algorithm that results in the mapping function or the algorithm that transforms the image colors. Color mapping is also sometimes called ''color transfer'' or, when grayscale images are involved, ''brightness transfer function (BTF)''; it may also be called ''photometric camera calibration'' or ''radiometric camera calibration''. Algorithms There are two types of color mapping algorithms: those that employ the statistics of the colors of two images, and those that rely on a given pixel correspondence between the images. An example of an algorithm that employs the statistical properties of the images is histogram matching. This is a classic algorithm for color mapping, suffering from the problem of sensitivity to image content differences. Newer statistic-based algorithms deal with this problem. An example of such ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Haralick Feature
Robert M. Haralick (born 1943) is Distinguished Professor in Computer Science at Graduate Center of the City University of New York (CUNY). Haralick is one of the leading figures in computer vision, pattern recognition, and image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow and past president of the International Association for Pattern Recognition. Prof. Haralick is the ''King-Sun Fu Prize winner of 2016'', "for contributions in image analysis, including remote sensing, texture analysis, mathematical morphology, consistent labeling, and system performance evaluation". Biography Haralick received a B.A. degree in mathematics from the University of Kansas in 1964, a B.S. degree in electrical engineering in 1966, and a M.S. degree in electrical engineering in 1967. In 1969, after completing his Ph.D. at the University of Kansas, he joined the faculty of the electrical engineering department, serving as professor from 1975 to 1 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robert Haralick
Robert M. Haralick (born 1943) is Distinguished Professor in Computer Science at Graduate Center of the City University of New York (CUNY). Haralick is one of the leading figures in computer vision, pattern recognition, and image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow and past president of the International Association for Pattern Recognition. Prof. Haralick is the ''King-Sun Fu Prize winner of 2016'', "for contributions in image analysis, including remote sensing, texture analysis, mathematical morphology, consistent labeling, and system performance evaluation". Biography Haralick received a B.A. degree in mathematics from the University of Kansas in 1964, a B.S. degree in electrical engineering in 1966, and a M.S. degree in electrical engineering in 1967. In 1969, after completing his Ph.D. at the University of Kansas, he joined the faculty of the electrical engineering department, serving as professor from 1975 to 1 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rotational Invariance
In mathematics, a function defined on an inner product space is said to have rotational invariance if its value does not change when arbitrary rotations are applied to its argument. Mathematics Functions For example, the function :f(x,y) = x^2 + y^2 is invariant under rotations of the plane around the origin, because for a rotated set of coordinates through any angle ''θ'' :x' = x \cos \theta - y \sin \theta :y' = x \sin \theta + y \cos \theta the function, after some cancellation of terms, takes exactly the same form :f(x',y') = ^2 + ^2 The rotation of coordinates can be expressed using matrix form using the rotation matrix, :\begin x' \\ y' \\ \end = \begin \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \\ \end\begin x \\ y \\ \end. or symbolically x′ = Rx. Symbolically, the rotation invariance of a real-valued function of two real variables is :f(\mathbf') = f(\mathbf) = f(\mathbf) In words, the function of the rotated coordinates takes exactly ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wavelet Transforms
In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform. Definition A function \psi \,\in\, L^2(\mathbb) is called an orthonormal wavelet if it can be used to define a Hilbert basis, that is a complete orthonormal system, for the Hilbert space L^2\left(\mathbb\right) of square integrable functions. The Hilbert basis is constructed as the family of functions \ by means of dyadic translations and dilations of \psi\,, :\psi_(x) = 2^\frac \psi\left(2^jx - k\right)\, for integers j,\, k \,\in\, \mathbb. If under the standard inner product on L^2\left(\mathbb\right), :\langle f, g\rangle = \int_^\infty f(x)\overlinedx this family is orthonormal, it is an orthonormal system: :\begin \langle\psi_,\psi_\rangle &= \int_^\infty \psi_(x)\overlinedx \\ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Model Fitting
Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. A related topic is regression analysis, which focuses more on questions of statistical inference such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of a function where no data are available, and to summarize the relationships among two or more variables. Extrapolation refers to the use of a fitted curve beyond the range of the observed data, and is subject to a degree of uncertainty since it may reflect the method used to construct the curve as much as it reflects the observed data. For linear-algebraic analysis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
