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 ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ...
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Feature Learning
In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Feature learning can be either supervised, unsupervised or self-supervised. * In supervised feature learning, features are learned using labeled input data. Labeled data includes input-label pairs where the ...
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Articles With Example Pseudocode
Article often refers to: * Article (grammar), a grammatical element used to indicate definiteness or indefiniteness * Article (publishing), a piece of nonfictional prose that is an independent part of a publication Article may also refer to: Government and law * Article (European Union), articles of treaties of the European Union * Articles of association, the regulations governing a company, used in India, the UK and other countries * Articles of clerkship, the contract accepted to become an articled clerk * Articles of Confederation, the predecessor to the current United States Constitution *Article of Impeachment, a formal document and charge used for impeachment in the United States * Articles of incorporation, for corporations, U.S. equivalent of articles of association * Articles of organization, for limited liability organizations, a U.S. equivalent of articles of association Other uses * Article, an HTML element, delimited by the tags and * Article of clothing, an ...
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Face Recognition
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless process. Facial recognition systems have been deployed in advanced human–compu ...
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Object Recognition And Categorization
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) * Geons (Irving Biederman) * Dickinson, Forsyth and Ponce Appearance-based methods * Use example images (called templates or exemplars) of the objects to perform recognition * Objects look different ...
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Kanade–Lucas–Tomasi Feature Tracker
In computer vision, the Kanade–Lucas–Tomasi (KLT) feature tracker is an approach to feature extraction. It is proposed mainly for the purpose of dealing with the problem that traditional image registration techniques are generally costly. KLT makes use of spatial intensity information to direct the search for the position that yields the best match. It is faster than traditional techniques for examining far fewer potential matches between the images. The registration problem The traditional image registration problem can be characterized as follows: Given two functions F(x) and G(x), representing pixel values at each location x in two images, respectively, where x is a vector. We wish to find the disparity vector h that minimizes some measure of the difference between F(x+h) and G(x), for x in some region of interest R. Some measures of the difference between F(x+h) and G(x): * L1 norm = \sum_\left\vert F(x+h)-G(x) \right\vert * L2 norm = \sqrt * Negative of normalized correla ...
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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. 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'', ...
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Steerable Filter
In applied mathematics, a steerable filter is an orientation-selective convolution kernel used for image enhancement and feature extraction that can be expressed via a linear combination of a small set of rotated versions of itself. As an example, the oriented first derivative of a 2D Gaussian is a steerable filter. The oriented first order derivative can be obtained by taking the dot product of a unit vector oriented in a specific direction with the gradient. The basis filters are the partial derivatives of a 2D Gaussian with respect to x and y. The process by which the oriented filter is synthesized at any given angle is known as ''steering'', which is used in similar sense as in beam steering for antenna arrays. Applications of steerable filters include edge detection Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. Th ...
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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 ...
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Haar Feature That Looks Similar To The Eye Region Which Is Darker Than The Upper Cheeks Is Applied Onto A Face
Haar may refer to: * Haar (fog), fog or sea mist (Scottish English) * Haar, Bavaria, a municipality near Munich, Germany * Haar (Westphalia), a hill range in North Rhine-Westphalia, Germany People with the surname * Alfréd Haar (1885–1933), Hungarian mathematician * Jarrod Haar, New Zealand organisational psychology academic See also * De Haar (other) * Haar wavelet, the first wavelet * Haar measure, a set-theoretic measure * 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 ...

Haar Feature That Looks Similar To The Bridge Of The Nose Is Applied Onto The Face
Haar may refer to: * Haar (fog), fog or sea mist (Scottish English) * Haar, Bavaria, a municipality near Munich, Germany * Haar (Westphalia), a hill range in North Rhine-Westphalia, Germany People with the surname * Alfréd Haar (1885–1933), Hungarian mathematician * Jarrod Haar, New Zealand organisational psychology academic See also * De Haar (other) * Haar wavelet, the first wavelet * Haar measure, a set-theoretic measure * 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 ...

Haar Feature Examples
Haar may refer to: * Haar (fog), fog or sea mist (Scottish English) * Haar, Bavaria, a municipality near Munich, Germany * Haar (Westphalia), a hill range in North Rhine-Westphalia, Germany People with the surname * Alfréd Haar (1885–1933), Hungarian mathematician * Jarrod Haar, New Zealand organisational psychology academic See also * De Haar (other) * Haar wavelet, the first wavelet * Haar measure, a set-theoretic measure * 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 ...