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Oriented FAST And Rotated BRIEF
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is based on the FAST keypoint detector and a modified version of the visual descriptor BRIEF (Binary Robust Independent Elementary Features). Its aim is to provide a fast and efficient alternative to SIFT. See also * Scale-invariant feature transform (SIFT) * Gradient Location and Orientation Histogram * LESH - Local Energy based Shape Histogram * Blob detection * Feature detection (computer vision) In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as poi ... References {{ reflist, refs {{cite conference , author = Rublee, Ethan , author2 = Rabaud, Vincent , a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, 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 forms of decisions. Understanding in this context means the transformation of visual images (the input of 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Object Recognition
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades. Approaches based on CAD-like object models * Edge detection * Primal sketch * Marr, Mohan and Nevatia * Lowe * Olivier Faugeras Recognition by parts * Generalized cylinders (Thomas Binford) * Geon (psychology), Geons (Irving Biederman) * Dickinson, Forsyth and Ponce Appearance-based methods * Use example images (called templates or exemplars) of the objects to perform recognition * Objects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
3D Reconstruction
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as spatio-temporal reconstruction, non-rigid or spatio-temporal reconstruction. Motivation and applications The research of 3D reconstruction has always been a difficult goal. By Using 3D reconstruction one can determine any object's 3D profile, as well as knowing the 3D coordinate of any point on the profile. The 3D reconstruction of objects is a generally scientific problem and core technology of a wide variety of fields, such as Computer Aided Geometric Design (CAGD), computer graphics, computer animation, computer vision, medical imaging, computational science, virtual reality, digital media, etc. For instance, the lesion information of the patients can be presented in 3D on the computer, which offers a new ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Features From Accelerated Segment Test
Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. The most promising advantage of the FAST corner detector is its computational efficiency. Referring to its name, it is indeed faster than many other well-known feature extraction methods, such as difference of Gaussians (DoG) used by the SIFT, SUSAN and Harris detectors. Moreover, when machine learning techniques are applied, superior performance in terms of computation time and resources can be realised. The FAST corner detector is very suitable for real-time video processing application because of this high-speed performance. Segment test detector FAST corner detector uses a circle of 16 pixels (a Bresenham circle of radius 3) to classify whether a candidate point p is actual ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BRIEF
Brief, briefs, or briefing may refer to: Documents * A letter * A briefing note * Papal brief, a papal letter less formal than a bull, sealed with the pope's signet ring or stamped with the device borne on this ring * Design brief, a type of educational or business document including desires and requirements * Creative brief, a document used by creative professionals and agencies to develop creative deliverables * Brief (architecture), a type of educational or business document including desires and requirements * Brief (law), a number of formal document types Computing * Brief (text editor), a popular text editor for the MS-DOS operating system Entertainment * Dr. Briefs, a fictional character in the Dragon Ball manga and anime * The Briefs, a Seattle band * Brief, a fictional character in the ''Panty & Stocking with Garterbelt'' anime Other * Brief, a garden in Sri Lanka designed by Landscape Architect Bevis Bawa * Briefs, a type of underwear and swimwear * ''The Briefing'', ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 transf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
GLOH
GLOH (Gradient Location and Orientation Histogram) is a robust image descriptor that can be used in computer vision tasks. It is a SIFT-like descriptor that considers more spatial regions for the histograms. An intermediate vector is computed from 17 location and 16 orientation bins, for a total of 272-dimensions. Principal components analysis (PCA) is then used to reduce the vector size to 128 (same size as SIFT descriptor vector). See also * Scale-invariant feature transform * Speeded Up Robust Features * LESH – Local Energy-based Shape Histogram * Feature detection (computer vision) In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as poi ... References Krystian Mikolajczyk and Cordelia Schmid "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LESH
Lesh is both a surname and a given name. Notable people with the name include: Surname: * Fyodor Lesh (1840 - 1903), physician * Pavel Lesh (1887 - 1915), sport shooter * Phil Lesh (born 1940), musician * Richard Lesh, professor * Ulysses Samuel Lesh (1868-1965), politician Given name: * Lesh Shkreli (born 1957), footballer See also * Lesch, a surname * Loesch * Losch * LESH * Phil Lesh and Friends * Lezhë Lezhë (, sq-definite, Lezha) is a city in the Republic of Albania and seat of Lezhë County and Lezhë Municipality. One of the main strongholds of the Labeatai, the earliest of the fortification walls of Lezhë are of typical Illyrian const ... {{given name, type=both Albanian masculine given names ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is convolution. Given some property of interest expressed as a function of position on the image, there are two main classes of blob detectors: (i) '' differential methods'', which are based on derivatives of the function with respect to position, and (ii) ''methods based on local extrema'', which are based on finding the local maxima and minima of the function. With the more recent terminology used in the field, these detectors can also be referred to as ''interest point operators'', or alternatively interest region operators (see also interest point detectio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Detection (computer Vision)
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions. More broadly a ''feature'' is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at ha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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