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Harris Affine Region Detector
In the fields of computer vision and image analysis, the Harris affine region detector belongs to the category of feature detection. Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or interest points so to make correspondences between images, recognize textures, categorize objects or build panoramas. Overview The Harris affine detector can identify similar regions between images that are related through affine transformations and have different illuminations. These ''affine-invariant'' detectors should be capable of identifying similar regions in images taken from different viewpoints that are related by a simple geometric transformation: scaling, rotation and shearing. These detected regions have been called both ''invariant'' and ''covariant''. On one hand, the regions are detected ''invariant'' of the image transformation but the regions ''covariantly'' change with image transformation. Do not dwell too much on t ...
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Computer Vision
Computer vision tasks include methods for image sensor, acquiring, Image processing, processing, Image analysis, 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 form of decisions. "Understanding" in this context signifies the transformation of visual images (the input to 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 theory behind artificial systems that extract information from images. Image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanning, 3D scanner, 3D point clouds ...
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Convolution
In mathematics (in particular, functional analysis), convolution is a operation (mathematics), mathematical operation on two function (mathematics), functions f and g that produces a third function f*g, as the integral of the product of the two functions after one is reflected about the y-axis and shifted. The term ''convolution'' refers to both the resulting function and to the process of computing it. The integral is evaluated for all values of shift, producing the convolution function. The choice of which function is reflected and shifted before the integral does not change the integral result (see #Properties, commutativity). Graphically, it expresses how the 'shape' of one function is modified by the other. Some features of convolution are similar to cross-correlation: for real-valued functions, of a continuous or discrete variable, convolution f*g differs from cross-correlation f \star g only in that either f(x) or g(x) is reflected about the y-axis in convolution; thus i ...
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Hessian-affine
The Hessian affine region detector is a feature detector used in the fields of computer vision and image analysis. Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points. The Hessian affine detector is part of the subclass of feature detectors known as ''affine-invariant'' detectors: Harris affine region detector, Hessian affine regions, maximally stable extremal regions, Kadir–Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based (IBR) regions. Algorithm description The Hessian affine detector algorithm is almost identical to the Harris affine region detector. In fact, both algorithms were derived bKrystian MikolajczykanCordelia Schmidin 2002, based on earlier work in, see also for a more general overview. How does the Hessian affine differ? The Harris affine detector relies on interest points detected at multiple scales using t ...
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Miko04 2
A , or shrine maiden,Groemer, 28. is a young priestess who works at a Shinto shrine. were once likely seen as shamans,Picken, 140. but are understood in modern Japanese culture to be an institutionalized role in daily life, trained to perform tasks ranging from sacred cleansing to performing the sacred dance. Appearance The traditional attire of a is a pair of red (divided, pleated trousers), a white (a predecessor of the kimono), and some white or red hair ribbons. In Shinto, the color white symbolizes purity. The garment put over the during dances is called a . Traditional tools include the , the (offertory -tree branches), and the . also use bells, drums, candles, , and bowls of rice in ceremonies. Definition The Japanese words and ("female shaman" and "shrine maiden" respectively)Kokugo Dai Jiten Dictionary, Revised edition, Shogakukan, 1988. are usually written as a compound of the kanji ("shaman"), and ("woman"). was archaically written ("''kami ...
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Hessian Affine
The Hessian affine region detector is a feature detector used in the fields of computer vision and image analysis. Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points. The Hessian affine detector is part of the subclass of feature detectors known as ''affine-invariant'' detectors: Harris affine region detector, Hessian affine regions, maximally stable extremal regions, Kadir–Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based (IBR) regions. Algorithm description The Hessian affine detector algorithm is almost identical to the Harris affine region detector. In fact, both algorithms were derived bKrystian MikolajczykanCordelia Schmidin 2002, based on earlier work in, see also for a more general overview. How does the Hessian affine differ? The Harris affine detector relies on interest points detected at multiple scales using the ...
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Miko Presentation CompareRegions
A , or shrine maiden,Groemer, 28. is a young priestess who works at a Shinto shrine. were once likely seen as shamans,Picken, 140. but are understood in modern Japanese culture to be an institutionalized role in daily life, trained to perform tasks ranging from sacred cleansing to performing the sacred dance. Appearance The traditional attire of a is a pair of red (divided, pleated trousers), a white (a predecessor of the kimono), and some white or red hair ribbons. In Shinto, the color white symbolizes purity. The garment put over the during dances is called a . Traditional tools include the , the (offertory -tree branches), and the . also use bells, drums, candles, , and bowls of rice in ceremonies. Definition The Japanese words and ("female shaman" and "shrine maiden" respectively)Kokugo Dai Jiten Dictionary, Revised edition, Shogakukan, 1988. are usually written as a compound of the kanji ("shaman"), and ("woman"). was archaically written ("''kami ...
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Overlap Region
Overlap may refer to: * In set theory, an overlap of elements shared between sets is called an intersection, as in a Venn diagram. * In music theory, overlap is a synonym for reinterpretation of a chord at the boundary of two musical phrases * Overlap (railway signalling), the length of track beyond a stop signal that is proved to be clear of obstructions as a safety margin * Overlap (road), a place where multiple road numbers overlap * Overlap (term rewriting), in mathematics, computer science, and logic, a property of the reduction rules in term rewriting systems * Overlap add, an efficient convolution method using FFT * Overlap coefficient, a similarity measure between sets * Orbital overlap, important concept in quantum mechanics describing a type of orbital interaction that affects bond strength * Overlap, publisher of the light novel series '' Arifureta: From Commonplace to World's Strongest'' Overlapping can refer to: * "Reaching over", term in Schenkerian theory, see Schen ...
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Homography (computer Vision)
In projective geometry, a homography is an isomorphism of projective spaces, induced by an isomorphism of the vector spaces from which the projective spaces derive. It is a bijection that maps lines to lines, and thus a collineation. In general, some collineations are not homographies, but the fundamental theorem of projective geometry asserts that is not so in the case of real projective spaces of dimension at least two. Synonyms include projectivity, projective transformation, and projective collineation. Historically, homographies (and projective spaces) have been introduced to study perspective and projections in Euclidean geometry, and the term ''homography'', which, etymologically, roughly means "similar drawing", dates from this time. At the end of the 19th century, formal definitions of projective spaces were introduced, which extended Euclidean and affine spaces by the addition of new points called points at infinity. The term "projective transformation" originated in ...
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Miko04 1
A , or shrine maiden,Groemer, 28. is a young priestess who works at a Shinto shrine. were once likely seen as shamans,Picken, 140. but are understood in modern Japanese culture to be an institutionalized role in daily life, trained to perform tasks ranging from sacred cleansing to performing the sacred dance. Appearance The traditional attire of a is a pair of red (divided, pleated trousers), a white (a predecessor of the kimono), and some white or red hair ribbons. In Shinto, the color white symbolizes purity. The garment put over the during dances is called a . Traditional tools include the , the (offertory -tree branches), and the . also use bells, drums, candles, , and bowls of rice in ceremonies. Definition The Japanese words and ("female shaman" and "shrine maiden" respectively)Kokugo Dai Jiten Dictionary, Revised edition, Shogakukan, 1988. are usually written as a compound of the kanji ("shaman"), and ("woman"). was archaically written ("''kami ...
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Scale-invariant Feature Transform
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 t ...
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