Pattern recognition in the context of "Constraint (information theory)"

The visual system is the physiological basis of visual perception (the ability to detect and process light). The system detects, transduces and interprets information concerning light within the visible range to construct an image and build a mental model of the surrounding environment. The visual system is associated with the eye and functionally divided into the optical system (including cornea and lens) and the neural system (including the retina and visual cortex).

The visual system performs a number of complex tasks based on the image forming functionality of the eye, including the formation of monocular images, the neural mechanisms underlying stereopsis and assessment of distances to (depth perception) and between objects, motion perception, pattern recognition, accurate motor coordination under visual guidance, and colour vision. Together, these facilitate higher order tasks, such as object identification. The neuropsychological side of visual information processing is known as visual perception, an abnormality of which is called visual impairment, and a complete absence of which is called blindness. The visual system also has several non-image forming visual functions, independent of visual perception, including the pupillary light reflex and circadian photoentrainment.

Cluster analysis, or clustering, is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group (called a cluster) exhibit greater similarity to one another (in some specific sense defined by the analyst) than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.

Cluster analysis refers to a family of algorithms and tasks rather than one specific algorithm. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.

Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition.

Different from applied behavior analysis from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups and/or organizations.

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, actuarial science, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.

Hematoxylin and eosin stain (or haematoxylin and eosin stain or hematoxylin–eosin stain; often abbreviated as H&E stain or HE stain) is one of the principal tissue stains used in histology. It is the most widely used stain in medical diagnosis and is often the gold standard. For example, when a pathologist looks at a biopsy of a suspected cancer, the histological section is likely to be stained with H&E.

H&E is the combination of two histological stains: hematoxylin and eosin. The hematoxylin stains cell nuclei a purplish blue, and eosin stains the extracellular matrix and cytoplasm pink, with other structures taking on different shades, hues, and combinations of these colors. Hence a pathologist can easily differentiate between the nuclear and cytoplasmic parts of a cell, and additionally, the overall patterns of coloration from the stain show the general layout and distribution of cells and provides a general overview of a tissue sample's structure. Thus, pattern recognition, both by expert humans themselves and by software that aids those experts (in digital pathology), provides histologic information.

Ulf Grenander (23 July 1923 – 12 May 2016) was a Swedish statistician and professor of applied mathematics at Brown University.

His early research was in probability theory, stochastic processes, time series analysis, and statistical theory (particularly the order-constrained estimation of cumulative distribution functions using his sieve estimator). In recent decades, Grenander contributed to computational statistics, image processing, pattern recognition, and artificial intelligence. He coined the term pattern theory to distinguish from pattern recognition.

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression.