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{{Feature detection (computer vision) navbox}} ...erator''' (or '''SFOP''') is an algorithm to [[Feature detection (computer vision)|detect local features]] in images. The algorithm was published by Förstner ...

{{short description|Relation of two images with software}} ...hey are rendered with the correct perspective and appear to have been part of the original scene (see [[Augmented reality]]). ...

...direction with the gradient. The basis filters are the partial derivatives of a 2D Gaussian with respect to <math>x</math> and <math>y</math>. ...in similar sense as in [[beam steering]] for antenna arrays. Applications of steerable filters include [[edge detection]], oriented texture analysis and ...

Matrixizing may be regarded as a generalization of the mathematical concept of [[Vectorization (mathematics)|vectorizing]]. The mode-''m'' matrixizing of tensor <math>{\mathcal A} \in {\mathbb C}^{I_0\times I_1\times\cdots\times ...

| header = Example of census transform | alt1 = Image of glasses and bottles ...

...of these functions. More formally, the subject of size theory is the study of the [[natural pseudodistance]] between [[size pair]]s. A survey of size theory can be found in ...

{{Feature detection (computer vision) navbox}} ...act the edges in respect to its direction. A combined use of compass masks of different directions could detect the edges from different angles. ...

...e signals, which are a mixture of these sources. SSA allows the separation of the stationary from the non-stationary sources in an observed time series. ...ies <math>x(t)</math> is assumed to be generated as a linear superposition of stationary sources <math>s^\mathfrak{s}(t)</math> and non-stationary source ...

...rential geometry]], and the [[curvature]] is often studied from this point of view.<ref>{{harvnb|Guggenheimer|1977}}</ref> Differential invariants were i ...than Lie's methods of differential invariants, it always yields invariants of the geometrical kind. ...

...]. Specifically the '''PCBR''' detector is designed for object recognition applications. From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as n ...

{{Short description|Extraction of 3D data from digital images}} ...ns of objects in the two panels. This is similar to the biological process of [[stereopsis]]. ...

{{short description|Method of analyzing large data sets}} ...^{I_0\times I_1\times \dots I_c\times \dots I_C}</math>. The proper choice of data organization into ''(C+1)''-way array, and analysis techniques can rev ...

...and M. S. Nixon, "Zernike Velocity Moments for Description and Recognition of Moving Shapes", Proc. BMVC 2001, Manchester, UK, 2:pp. 705-714, 2001</ref> A Cartesian moment of a single image is calculated by ...

{{Short description|Computer vision algorithm}} ...mproved and adopted in many algorithms to preprocess images for subsequent applications. ...

{{Short description|Family of computer vision models designed for efficient inference on mobile devices}} ...ned for [[image classification]], [[object detection]], and other computer vision tasks. They are designed for small size, low latency, and low power consump ...

...sis give a most probable action. This technique is widely used in the area of [[artificial intelligence]]. ...texture and so on, which can be detected by [[feature detection (computer vision)|feature detection]] methods. ...

{{Short description|Series of convolutional neural networks for image classification}} ...]s (CNNs) developed by the Visual Geometry Group (VGG) at the [[University of Oxford]]. ...

...otropic diffusion is a ''non-linear'' and ''space-variant'' transformation of the original image. ...e diffusion coefficient, instead of being a constant scalar, is a function of image position and assumes a [[matrix (mathematics)|matrix]] (or [[tensor]] ...

...r vision)|feature extraction]]. This article provides a unified discussion of the role that chessboards play in the canonical methods from these two area ...2D) images of it.<ref name=forsyth2002>D. Forsyth and J. Ponce. ''Computer Vision: A Modern Approach''. Prentice Hall. (2002). {{ISBN|978-0262061582}}.</ref> ...

...ionship of the nearby pixels, which is also called neighbourhood. The goal of this approach is to classify the images by using the contextual information As the image illustrated below, if only a small portion of the image is shown, it is very difficult to tell what the image is about. ...