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Histogram Of Oriented Gradients
The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy. Robert K. McConnell of Wayland Research Inc. first described the concepts behind HOG without using the term HOG in a patent application in 1986. In 1994 the concepts were used by Mitsubishi Electric Research Laboratories. However, usage only became widespread in 2005 when Navneet Dalal and Bill Triggs, researchers for the French National Institute for Research in Computer Science and Automation (INRIA), presented their supplementary work on HOG descriptors at the Conf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Descriptor
In computer vision, visual descriptors or image descriptors are descriptions of the Feature (computer vision), visual features of the contents in images, videos, or algorithms or applications that produce such descriptions. They describe elementary characteristics such as the shape, the color, the Texture (computer graphics), texture or the Motion (physics), motion, among others. Introduction As a result of the new communication technologies and the massive use of Internet in our society, the amount of audio-visual information available in digital format is increasing considerably. Therefore, it has been necessary to design some systems that allow us to describe the content of several types of multimedia information in order to search and classify them. The audio-visual descriptors are in charge of the contents description. These descriptors have a good knowledge of the objects and events found in a video, image or sound, audio and they allow the quick and efficient searches of th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sobel Operator
The Sobel operator, sometimes called the Sobel–Feldman operator or Sobel filter, is used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges. It is named after Irwin Sobel and Gary Feldman, colleagues at the Stanford Artificial Intelligence Laboratory (SAIL). Sobel and Feldman presented the idea of an " Isotropic 3 × 3 Image Gradient Operator" at a talk at SAIL in 1968. Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function. At each point in the image, the result of the Sobel–Feldman operator is either the corresponding gradient vector or the norm of this vector. The Sobel–Feldman operator is based on convolving the image with a small, separable, and integer-valued filter in the horizontal and vertical directions and is therefore relatively inexpensive in terms of computations. On the other hand, the grad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hog Image05
Hog may refer to: Animals * Pig ** Usually referring to the domestic pig ** Sometimes referring to other animals in the family Suidae, including: *** Warthog *** Red river hog *** Giant forest hog * groundhog * hedgehog * hog (sheep), a yearling sheep, as yet unshorn Other uses * Harley-Davidson, a motorcycle manufacturer ** Harley Owners Group * The Hogs (American football), a prior nickname for the offensive line of the Washington Redskins * Hogging and sagging, a nautical term * Hogging (sexual practice) * Higher order grammar * Histogram of oriented gradients, used in computer vision and image processing for the purpose of object detection * House of Guitars * Arkansas Razorbacks, the sports teams of the University of Arkansas * Frank País Airport, IATA symbol HOG * Hidden Object Game, a genre of casual puzzle games * Hogarthian or Hog, a scuba diving gear configuration pioneered by William Hogarth Main See also * Sandhog, the slang term given to urban miners, construction w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hog Image04
Hog may refer to: Animals * Pig ** Usually referring to the domestic pig ** Sometimes referring to other animals in the family Suidae, including: *** Warthog *** Red river hog *** Giant forest hog * groundhog * hedgehog * hog (sheep), a yearling sheep, as yet unshorn Other uses * Harley-Davidson, a motorcycle manufacturer ** Harley Owners Group * The Hogs (American football), a prior nickname for the offensive line of the Washington Redskins * Hogging and sagging, a nautical term * Hogging (sexual practice) * Higher order grammar * Histogram of oriented gradients, used in computer vision and image processing for the purpose of object detection * House of Guitars * Arkansas Razorbacks, the sports teams of the University of Arkansas * Frank País Airport, IATA symbol HOG * Hidden Object Game, a genre of casual puzzle games * Hogarthian or Hog, a scuba diving gear configuration pioneered by William Hogarth Main See also * Sandhog, the slang term given to urban miners, construction w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
False Positive Rate
In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm ratio) is the probability of falsely rejecting the null hypothesis for a particular test. The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio. Definition The false positive rate is FPR=\frac where \mathrm is the number of false positives, \mathrm is the number of true negatives and N=\mathrm+\mathrm is the total number of ground truth negatives. The level of significance that is used to test each hypothesis is set based on the form of inference ( simultaneous inference vs. selective inference) and its supporting criteria (for example FWER or FDR), that were pre-determined by the researche ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hog Image03
Hog may refer to: Animals * Pig ** Usually referring to the domestic pig ** Sometimes referring to other animals in the family Suidae, including: *** Warthog *** Red river hog *** Giant forest hog * groundhog * hedgehog * hog (sheep), a yearling sheep, as yet unshorn Other uses * Harley-Davidson, a motorcycle manufacturer ** Harley Owners Group * The Hogs (American football), a prior nickname for the offensive line of the Washington Redskins * Hogging and sagging, a nautical term * Hogging (sexual practice) * Higher order grammar * Histogram of oriented gradients, used in computer vision and image processing for the purpose of object detection * House of Guitars * Arkansas Razorbacks, the sports teams of the University of Arkansas * Frank País Airport, IATA symbol HOG * Hidden Object Game, a genre of casual puzzle games * Hogarthian or Hog, a scuba diving gear configuration pioneered by William Hogarth Main See also * Sandhog, the slang term given to urban miners, construction w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Massachusetts Institute Of Technology
The Massachusetts Institute of Technology (MIT) is a private land-grant research university in Cambridge, Massachusetts. Established in 1861, MIT has played a key role in the development of modern technology and science, and is one of the most prestigious and highly ranked academic institutions in the world. Founded in response to the increasing industrialization of the United States, MIT adopted a European polytechnic university model and stressed laboratory instruction in applied science and engineering. MIT is one of three private land grant universities in the United States, the others being Cornell University and Tuskegee University. The institute has an urban campus that extends more than a mile (1.6 km) alongside the Charles River, and encompasses a number of major off-campus facilities such as the MIT Lincoln Laboratory, the Bates Center, and the Haystack Observatory, as well as affiliated laboratories such as the Broad and Whitehead Institutes. , 98 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Component Analysis
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Haar Wavelet
In mathematics, the Haar wavelet is a sequence of rescaled "square-shaped" functions which together form a wavelet family or basis. Wavelet analysis is similar to Fourier analysis in that it allows a target function over an interval to be represented in terms of an orthonormal basis. The Haar sequence is now recognised as the first known wavelet basis and is extensively used as a teaching example. The Haar sequence was proposed in 1909 by Alfréd Haar. Haar used these functions to give an example of an orthonormal system for the space of square-integrable functions on the unit interval , 1 The study of wavelets, and even the term "wavelet", did not come until much later. As a special case of the Daubechies wavelet, the Haar wavelet is also known as Db1. The Haar wavelet is also the simplest possible wavelet. The technical disadvantage of the Haar wavelet is that it is not continuous, and therefore not differentiable. This property can, however, be an advantage for th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PCA-SIFT
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local ''features'' in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalised Hough transf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generalized Haar Wavelet
A generalization is a form of abstraction whereby common properties of specific instances are formulated as general concepts or claims. Generalizations posit the existence of a domain or set of elements, as well as one or more common characteristics shared by those elements (thus creating a conceptual model). As such, they are the essential basis of all valid deductive inferences (particularly in logic, mathematics and science), where the process of verification is necessary to determine whether a generalization holds true for any given situation. Generalization can also be used to refer to the process of identifying the parts of a whole, as belonging to the whole. The parts, which might be unrelated when left on their own, may be brought together as a group, hence belonging to the whole by establishing a common relation between them. However, the parts cannot be generalized into a whole—until a common relation is established among ''all'' parts. This does not mean that the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support Vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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