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Haar-like Feature
Haar-like features are digital image features used in object recognition. They owe their name to their intuitive similarity with Haar wavelets and were used in the first real-time face detector.Viola and Jones,Rapid object detection using a boosted cascade of simple features, Computer Vision and Pattern Recognition, 2001 Historically, working with only image intensities (i.e., the RGB pixel values at each and every pixel of image) made the task of feature calculation computationally expensive. A publication by Papageorgiou et al. discussed working with an alternate feature set based on Haar wavelets instead of the usual image intensities. Paul Viola and Michael Jones adapted the idea of using Haar wavelets and developed the so-called Haar-like features. A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. This difference is then used to c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Digital Image
A digital image is an image composed of picture elements, also known as ''pixels'', each with ''finite'', '' discrete quantities'' of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with ''x'', ''y'' on the x-axis and y-axis, respectively. Depending on whether the image resolution is fixed, it may be of vector or raster type. Raster Raster images have a finite set of digital values, called ''picture elements'' or pixels. The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding antiquated values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Viola–Jones Object Detection Framework
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. The algorithm is efficient for its time, able to detect faces in 384 by 288 pixel images at 15 frames per second on a conventional 700 MHz Intel Pentium III. It is also robust, achieving high precision and recall. While it has lower accuracy than more modern methods such as convolutional neural network, its efficiency and compact size (only around 50k parameters, compared to millions of parameters for typical CNN like DeepFace) means it is still used in cases with limited computational power. For example, in the original paper, they reported that this face detector could run on the Compaq iPaq at 2 fps (this device has a low power StrongARM without floating point hardware). Problem description Face det ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rounding Error
A roundoff error, also called rounding error, is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. Rounding errors are due to inexactness in the representation of real numbers and the arithmetic operations done with them. This is a form of quantization error. When using approximation equations or algorithms, especially when using finitely many digits to represent real numbers (which in theory have infinitely many digits), one of the goals of numerical analysis is to estimate computation errors. Computation errors, also called numerical errors, include both truncation errors and roundoff errors. When a sequence of calculations with an input involving any roundoff error are made, errors may accumulate, sometimes dominating the calculation. In ill-conditioned problems, significant error may accumulate. In short, there are two major facets of roundoff errors i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimensionality
In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coordinate is needed to specify a point on itfor example, the point at 5 on a number line. A surface, such as the boundary of a cylinder or sphere, has a dimension of two (2D) because two coordinates are needed to specify a point on itfor example, both a latitude and longitude are required to locate a point on the surface of a sphere. A two-dimensional Euclidean space is a two-dimensional space on the plane. The inside of a cube, a cylinder or a sphere is three-dimensional (3D) because three coordinates are needed to locate a point within these spaces. In classical mechanics, space and time are different categories and refer to absolute space and time. That conception of the world is a four-dimensional space but not the one that was found neces ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lookup Table
In computer science, a lookup table (LUT) is an array that replaces runtime computation with a simpler array indexing operation. The process is termed as "direct addressing" and LUTs differ from hash tables in a way that, to retrieve a value v with key k, a hash table would store the value v in the slot h(k) where h is a hash function i.e. k is used to compute the slot, while in the case of LUT, the value v is stored in slot k, thus directly addressable. The savings in processing time can be significant, because retrieving a value from memory is often faster than carrying out an "expensive" computation or input/output operation. The tables may be precalculated and stored in static program storage, calculated (or "pre-fetched") as part of a program's initialization phase ( memoization), or even stored in hardware in application-specific platforms. Lookup tables are also used extensively to validate input values by matching against a list of valid (or invalid) items in an array and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SIGGRAPH
SIGGRAPH (Special Interest Group on Computer Graphics and Interactive Techniques) is an annual conference on computer graphics (CG) organized by the ACM SIGGRAPH, starting in 1974. The main conference is held in North America; SIGGRAPH Asia, a second conference held annually, has been held since 2008 in countries throughout Asia. Overview The conference incorporates both academic presentations as well as an industry trade show. Other events at the conference include educational courses and panel discussions on recent topics in computer graphics and interactive techniques. SIGGRAPH Proceedings The SIGGRAPH conference proceedings, which are published in the ACM Transactions on Graphics, has one of the highest impact factors among academic publications in the field of computer graphics. The paper acceptance rate for SIGGRAPH has historically been between 17% and 29%, with the average acceptance rate between 2015 and 2019 of 27%. The submitted papers are peer-reviewed und ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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. In the image processing domain, it is also known as an integral image. It was introduced to computer graphics in 1984 by Frank Crow for use with mipmaps. In computer vision it was popularized by Lewis and then given the name "integral image" and prominently used within the Viola–Jones object detection framework in 2001. Historically, this principle is very well known in the study of multi-dimensional probability distribution functions, namely in computing 2D (or ND) probabilities (area under the probability distribution) from the respective cumulative distribution functions. The algorithm As the name suggests, the value at any point (''x'', ''y'') in the summed-area table is the sum of all the pixels above and to the left of (''x'', ''y''), inclusive: I(x,y) = \sum_ i(x',y') where i(x,y) is the value of the pixel at (''x'', ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Integral Image
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. In the image processing domain, it is also known as an integral image. It was introduced to computer graphics in 1984 by Frank Crow for use with mipmaps. In computer vision it was popularized by Lewis and then given the name "integral image" and prominently used within the Viola–Jones object detection framework in 2001. Historically, this principle is very well known in the study of multi-dimensional probability distribution functions, namely in computing 2D (or ND) probabilities (area under the probability distribution) from the respective cumulative distribution functions. The algorithm As the name suggests, the value at any point (''x'', ''y'') in the summed-area table is the sum of all the pixels above and to the left of (''x'', ''y''), inclusive: I(x,y) = \sum_ i(x',y') where i(x,y) is the value of the pixel at ('' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Categorization
Categorization is the ability and activity of recognizing shared features or similarities between the elements of the experience of the world (such as Object (philosophy), objects, events, or ideas), organizing and classifying experience by associating them to a more abstract group (that is, a category, class, or type), on the basis of their traits, features, similarities or other criteria that are Universal (metaphysics), universal to the group. Categorization is considered one of the most fundamental cognitive abilities, and as such it is studied particularly by psychology and cognitive linguistics. Categorization is sometimes considered synonymous with classification (cf., Classification (general theory)#Synonyms and near-synonyms, Classification synonyms). Categorization and classification allow humans to organize things, objects, and ideas that exist around them and simplify their understanding of the world. Categorization is something that humans and other organisms ''do ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature (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 h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Michael Jones (scientist)
Michael J. Jones is an American computer scientist and inventor working as a computer vision researcher at Mitsubishi Electric Research Laboratories. Education Jones earned a PhD from the Massachusetts Institute of Technology in 1997 under Tomaso Poggio. Career Jones is the co-inventor, with Paul Viola, of the Viola–Jones face detection method, an ICCV 2003 Marr Prize and CVPR Longuet-Higgins Prize The Conference on Computer Vision and Pattern Recognition (CVPR) is an annual conference on computer vision and pattern recognition, which is regarded as one of the most important conferences in its field. According to Google Scholar Metrics (2022 ... winner. References Year of birth missing (living people) Living people Computer vision researchers Mitsubishi Electric people Massachusetts Institute of Technology alumni American computer scientists {{compu-scientist-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
