Computer vision Computer vision An artificial neural network (ANN), also called a simulated neural network (SNN) (but the term neural network (NN) is grounded in biology and refers to very real, highly complex plexus), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. There is no precise agreed definition among researchers as to what a neural network is, but most would agree that it involves a network of simple processing elements ( neurons) which can exhibit complex global behaviour, determined by the connections between the processing elements and element parameters. Since anything approaching a full appreciation of neuronal function remains a distant dream, and since the factors producing global output result from many non-linear, modulating, and poorly understood real-time feedback signals within a single neuron, the greatly simplified artificial networks (where 'neurons' are modeled as input/output nodes) are perceived as academic research tools rather than even a distant representation of brain function. The original inspiration for the technique was from examination of the central nervous system and the neurons (and their axons, dendrites and synapses) which constitute one of its most significant information processing elements (see Neuroscience). In a neural network model, simple nodes (called variously "neurons", "neurodes", "PEs" ("processing elements") or "units") are connected together to form a network of nodes — hence the term "neural network". The term also includes implementations purely in software that may run on general purpose computers. ...more on Wikipedia about "Artificial neural network"
Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. ...more on Wikipedia about "Automatic image annotation"
Automatic number plate recognition (ANPR) is a mass surveillance method that uses optical character recognition on images to read the licence plates on vehicles. As of 2005 systems can scan number plates at around one per second on cars travelling up to 100 mph (160 km/h). They can either use existing closed-circuit television or road-rule enforcement cameras, or ones specifically designed for the task. They are used by various police forces and as a method of electronic toll collection on pay-per-use roads, and monitoring traffic activity such as red light adherence in an intersection. ...more on Wikipedia about "Automatic number plate recognition"
In statistics, the Bhattacharyya coefficient is a measure of the statistical separability of classes, giving an estimate of the probability of correct classification. More generally, it is a measure of the distance between two discrete distributions. For discrete distributions and over the same domain : ...more on Wikipedia about "Bhattacharyya coefficient"
Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting occurs in stages, by incrementally adding to the current learned function. At every stage, a weak learner (i.e., one that has an accuracy greater than chance) is trained with the data. The output of the weak learner is then added to the learned function, with some strength (proportional to how accurate the weak learner is). Then, the data is reweighted: examples that the current learned function gets wrong are "boosted" in importance, so that future weak learners will attempt to fix the errors. ...more on Wikipedia about "Boosting"
Camera tracking or matchmoving refers to the process of matching the position and angle of CGI (such as a spaceship or animated creature) to real footage shot with a film or video camera. This process can be accomplished manually, on a frame by frame basis. More common today is the use of automated matchmoving software based on computer vision techniques that can extract camera position and direction data over time from a piece of digitized footage with minimal user intervention. Done correctly, matchmoving allows the seamless compositing of CG and bluescreen elements into camera footage, even handheld camera footage. ...more on Wikipedia about "Camera tracking"
The Canny edge detection operator was developed by John F. Canny in 1986 and uses a multiple stage algorithm to detect a wide range of edges. Most importantly, Canny also produced a computational theory of edge detection explaining why the technique works. ...more on Wikipedia about "Canny" Be happy with shortopedia
Closed-circuit television (CCTV), as a collection of surveillance cameras doing video surveillance, is the use of television cameras for surveillance. It differs from broadcast television in that all components are directly linked via cables or other direct means. CCTV is often used in areas where there is an increased need for security, such as banks, casinos, and airports. The use of CCTVs in public places has increased, causing debate over security vs. privacy. ...more on Wikipedia about "Closed-circuit television"
The complex wavelet transform is a complex-valued extension to the standard discrete wavelet transform (DWT). It is a two-dimensional wavelet transform which provides multiresolution, sparse representation, and useful characterization of the structure of an image. Further, it purveys a high degree of shift-invariance in its magnitude. However, a drawback to this transform is that it is four-times redundant compared to a separable (DWT). ...more on Wikipedia about "Complex wavelet transform"
Computer vision is the study and application of methods which allow computers to "understand" image content or content of multidimensional data in general. The term "understand" means here that specific information is being extracted from the image data for a specific purpose: either for presenting it to a human operator (e. g., if cancerous cells have been detected in a microscopy image), or for controlling some process (e. g., an industry robot or an autonomous vehicle). The image data that is fed into a computer vision system is often a digital gray-scale or colour image, but can also be in the form of two or more such images (e. g., from a stereo camera pair), a video sequence, or a 3D volume (e. g., from a tomography device). In most practical computer vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. ...more on Wikipedia about "Computer vision"
The condensation algorithm (Conditional Density Propagation) is a computer vision algorithm. The principle application is to segment and track moving objects in a cluttered background. Image segmentation is one of the more basic and difficult aspects of computer vision and is generally a prerequisite to object recognition. Being able to identify which pixels in an image make up various objects is a non-trivial problem. As you might imagine, tracking a red ball bouncing around on a white background is a fairly easy problem. As scenes get more complex, tracking the object becomes increasingly difficult. Condensation is a probabilistic algorithm that proposes a solution to this problem. ...more on Wikipedia about "Condensation algorithm"
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. "Content-based" means that the search makes use of the contents of the images themselves, rather than relying on human-inputted metadata such as captions or keywords. A content-based image retrieval system (CBIRS) is a piece of software that implements CBIR. ...more on Wikipedia about "Content-based image retrieval"
A dendrogram is a tree diagram frequently used to illustrate the arrangement of the clusters produced by a clustering algorithm (see cluster analysis). ...more on Wikipedia about "Dendrogram"
Digital image processing is the use of computer algorithms to perform image processing on digital images. Digital image processing has the same advantages (over analog image processing) as digital signal processing has (over analog signal processing) -- it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. ...more on Wikipedia about "Digital image processing"
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In computer vision and image processing features are one of the basis for the processing of image data. The goal of computer vision is to extract information about the content of a specific image data, e.g., a 2D image. The extraction process is then often defined in terms of image features of different levels of complexity, ranging from low-level features such as edges or lines via medium-level features such as corners or junctions to high-level features in terms of objects or living beings, and actions which they perform. However, the classification of features into low, medium and high levels is not standardized, and the examples given here should only be taken as an indication of the general principle that features at higher levels or of higher complexity often are defined in terms of features at lower levels. This implies that we can organize the features in a feature hierarchy. ...more on Wikipedia about "Feature (Computer vision)"
Feature extraction is an area of image processing which involves using algorithms to detect and isolate various desired portions of a digitized image or video stream. ...more on Wikipedia about "Feature extraction"
A hidden Markov model (HMM) is a statistical model where the system being modelled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters, from the observable parameters, based on this assumption. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications. ...more on Wikipedia about "Hidden Markov model"
The horopter is a 3D curve that can be defined as the set of points in space for which the light falls on corresponding areas in the two retinas ** , that is, anatomically identical points. ...more on Wikipedia about "Horopter"
Image analysis is the extraction of information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person by its face. ...more on Wikipedia about "Image analysis"
Image moments for the scalar (greytone) image are calculated by considering the image as a probability density function. ...more on Wikipedia about "Image moments"
In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. ...more on Wikipedia about "Image processing"
In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements. ...more on Wikipedia about "Image registration"
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools. ...more on Wikipedia about "Image retrieval"
In between computer graphics and computer vision, Image-Based Modeling and Rendering (IBMR) methods rely on a set of images (Image-Based) of a scene to generate a three-dimensional model (Modeling) and/or some novel views ( Rendering (computer graphics)) of this scene. ...more on Wikipedia about "Image-Based Modeling And Rendering"
An iris scan is one of the most currently used methods of biometric authentication. Using a small camera, an iris scan system examines both irides of the individual's eyes. It then takes advantage of small details in the iris stromal pattern in order to attempt positive identification of an individual. Anthropologists have long established up to 400 items in the inherited feature space of the iris stroma and epithelium. The irides of identical twins are measurably different; There are differences between the left and the right eyes of an individual and differences in images of the same iris at different moments in time. ...more on Wikipedia about "Iris scan"
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