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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 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.
Pattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power.
Image analysis or imagery analysis is the extraction of meaningful 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 from their face.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
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.
Robert M. Haralick is Distinguished Professor in Computer Science at Graduate Center of the City University of New York (CUNY). Haralick is one of the leading figures in computer vision, pattern recognition, and image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow and past president of the International Association for Pattern Recognition. Professor Haralick is the King-Sun Fu Prize winner of 2016, "for contributions in image analysis, including remote sensing, texture analysis, mathematical morphology, consistent labeling, and system performance evaluation".
Video tracking is the process of locating a moving object over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Video tracking can be a time-consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use object recognition techniques for tracking, a challenging problem in its own right.
In computer science and machine learning, cellular neural networks (CNN) or cellular nonlinear networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs.
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.
The following outline is provided as an overview of and topical guide to computer vision:
Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems — "software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents."
Range segmentation is the task of segmenting (dividing) a range image, an image containing depth information for each pixel, into segments (regions), so that all the points of the same surface belong to the same region, there is no overlap between different regions and the union of these regions generates the entire image.
Active contour model, also called snakes, is a framework in computer vision introduced by Michael Kass, Andrew Witkin, and Demetri Terzopoulos for delineating an object outline from a possibly noisy 2D image. The snakes model is popular in computer vision, and snakes are widely used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.
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.
Nicolai Petkov is Dutch computer scientist, and professor of Intelligent Systems and Computer Science at the University of Groningen, known for his contributions in the fields of brain-inspired computing, pattern recognition, machine learning, and parallel computing.
In computer vision, rigid motion segmentation is the process of separating regions, features, or trajectories from a video sequence into coherent subsets of space and time. These subsets correspond to independent rigidly moving objects in the scene. The goal of this segmentation is to differentiate and extract the meaningful rigid motion from the background and analyze it. Image segmentation techniques labels the pixels to be a part of pixels with certain characteristics at a particular time. Here, the pixels are segmented depending on its relative movement over a period of time i.e. the time of the video sequence.
Subhasis Chaudhuri is an Indian electrical engineer and the director at the Indian Institute of Technology, Bombay. He is a former K. N. Bajaj Chair Professor of the Department of Electrical Engineering of IIT Bombay. He is known for his pioneering studies on computer vision and is an elected fellow of all the three major Indian science academies viz. the National Academy of Sciences, India, Indian Academy of Sciences, and Indian National Science Academy. He is also a fellow of Institute of Electrical and Electronics Engineers, and the Indian National Academy of Engineering. The Council of Scientific and Industrial Research, the apex agency of the Government of India for scientific research, awarded him the Shanti Swarup Bhatnagar Prize for Science and Technology, one of the highest Indian science awards, in 2004 for his contributions to Engineering Sciences.
The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is held annually since 2012. From the beginning it is held in conjunction with two other conferences: ICAART - International Conference on Agents and Artificial Intelligence and ICORES - International Conference on Operations Research and Enterprise Systems.
Gradient vector flow (GVF), a computer vision framework introduced by Chenyang Xu and Jerry L. Prince, is the vector field that is produced by a process that smooths and diffuses an input vector field. It is usually used to create a vector field from images that points to object edges from a distance. It is widely used in image analysis and computer vision applications for object tracking, shape recognition, segmentation, and edge detection. In particular, it is commonly used in conjunction with active contour model.
Mark S. Nixon is an author, researcher, editor and an academic. He is the former president of IEEE Biometrics Council, and former vice-Chair of IEEE PSPB. He retired from his position as Professor of Electronics and Computer Science at University of Southampton in 2019.
References
- K.W. Sum and Paul Y.S. Cheung, "Boundary Vector Field for Parametric Active Contours," Pattern Recognition, vol. 40, no. 6, pp. 1635–1645, Jun 2007
- Rafael Verdú-Monedero, Juan Morales-Sánchez, and Luis Weruaga, "Convergence Analysis of Active Contours," Image and Vision Computing, vol. 26, issue 8, pp. 1118–1128, 2008
- N. Lin and B. Hu, "Moving Object Detection and Tracking in Video Sequences Based on Boundary Vector Field," Journal of Computer Applications, vol. 28, Jun 2008
- Pattern Recognition (Journal of the Pattern Recognition Society)
- Image and Vision Computing, Elsevier Journal
- Journal of Computer Applications Archived 2008-12-18 at the Wayback Machine
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