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20 relations: Abscissa and ordinate, Blob detection, Computer vision, Determinant, European Conference on Computer Vision, Feature (computer vision), Feature detection (computer vision), Gaussian blur, GLOH, Haar-like feature, Hessian matrix, Image registration, Local energy-based shape histogram, Outline of object recognition, Pyramid (image processing), Scale-invariant feature transform, Speeded up robust features, Statistical classification, Summed-area table, 3D reconstruction.
Abscissa and ordinate
In mathematics, the abscissa (plural abscissae or abscissæ or abscissas) and the ordinate are respectively the first and second coordinate of a point in a coordinate system.
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Blob detection
In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared to surrounding regions.
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Computer vision
Computer vision is a field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
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Determinant
In linear algebra, the determinant is a value that can be computed from the elements of a square matrix.
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European Conference on Computer Vision
ECCV, the European Conference on Computer Vision, is a biennial research conference with the proceedings published by Springer Science+Business Media.
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Feature (computer vision)
In computer vision and image processing, a feature is a piece of information which is relevant for solving the computational task related to a certain application.
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Feature detection (computer vision)
In computer vision and image processing feature detection includes methods for computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not.
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Gaussian blur
In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss).
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GLOH
GLOH (Gradient Location and Orientation Histogram) is a robust image descriptor that can be used in computer vision tasks.
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Haar-like feature
Haar-like features are digital image features used in object recognition.
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Hessian matrix
In mathematics, the Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field.
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Image registration
Image registration is the process of transforming different sets of data into one coordinate system.
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Local energy-based shape histogram
Local energy-based shape histogram (LESH) is a proposed image descriptor in computer vision.
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Outline of object recognition
The following outline is provided as an overview of and topical guide to object recognition: Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence.
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Pyramid (image processing)
Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling.
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Scale-invariant feature transform
The scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images.
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Speeded up robust features
In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor.
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Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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Summed-area table
A summed-area table is a data structure and algorithm for quickly and efficiently generating the sum of values in a rectangular subset of a grid.
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3D reconstruction
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects.
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References
[1] https://en.wikipedia.org/wiki/Speeded_up_robust_features
