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Topic: Features (pattern recognition)

	Pattern recognition - Wikipedia, the free encyclopedia
		A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.
		Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system.
		Pattern recognition is studied in many fields, including psychology, ethology, and computer science.
	en.wikipedia.org /wiki/Pattern_recognition (560 words)

	K-nearest neighbor algorithm - Wikipedia, the free encyclopedia
		In pattern recognition, the k-nearest neighbour algorithm (k-NN) is a method for classifying objects based on closest training samples in the feature space.
		A particularly popular approach is the use of evolutionary algorithms to optimize feature scaling.
		Another popular approach is to scale features by the mutual information of the training data with the training classes.
	en.wikipedia.org /wiki/Nearest_neighbor_(pattern_recognition) (530 words)

	Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data (Site not responding. Last check: 2007-11-03)
		Consequently, applications of structural pattern recognition have been primarily restricted to domains in which the set of useful morphological features has been established in the literature (e.g., speech recognition and character recognition) and the syntactic grammars can be composed by hand (e.g., electrocardiogram diagnosis).
		Such a set of morphological features is suggested as the foundation for the development of a suite of structure detectors to perform generalized feature extraction for structural pattern recognition in time-series data.
		The classification accuracies achieved using the features extracted by the structure detectors were consistently as good as or better than the classification accuracies achieved when using the features generated by the statistical feature extractors, thus demonstrating that the suite of structure detectors effectively performs generalized feature extraction for structural pattern recognition in time-series data.
	www.cs.cmu.edu /~bobski/pubs/tr01108.html (486 words)

	Statistical Pattern Recognition
		Statistical pattern recognition is used to establish boundaries between patterns.
		Preprocessing Noise and extraneous data is removed from the input data and pattern is normalized and segmented from the background.
		Classification Based on the measured features from the learning stage, the classifier assigns the input pattern to one of the pattern classes.
	www.cs.utah.edu /vissim/papers/colorOrthoimagery/orthophoto-node4.html (348 words)

	Pattern Recognition (Site not responding. Last check: 2007-11-03)
		Pattern recognition thus usually consists of two phases: training and testing.
		New patterns are then classified by a simple rule: the same class as whichever prototype the unknown pattern is closes to in the feature space.
		This can be thought of as a three-class, three-feature pattern recognition problem, classifying each pixel as one of the three classes according to the values in the three different flavors of MRI.
	rivit.cs.byu.edu /morse/550-F95/node34.html (1596 words)

	Structural Pattern Recognition of Land Use
		One important feature of graphical structural pattern recognition, is that graph-theoretic approaches and concepts are used to represent the properties and relations of the pattern primitives.
		Finally, both are concerned with the performing a classification which assigns the pattern primitives present, on the basis of the their derived pattern features, to one of set of candidate pattern classes; often, this classification process involves some form of explicit spatial analytical functionality.
		The pattern features derived for each region pattern primitive may take a wide variety of forms and measurement scales, although in general, they tend to describe the morphology of regions (e.g., their area, perimeter, compactness etc) as well as the spatial relationships which exist between them (e.g., adjacency, containment, distance and direction etc).
	www.geocomputation.org /1998/43/gc_43.htm (4199 words)

	Conditional and Unconditional Independence Assumptions in Pattern Recognition (Site not responding. Last check: 2007-11-03)
		In order to simplify the classification problem, features are chosen to (1) cluster the patterns in their respective classes, and (2) separate the classes.
		That is, features are extracted from a pair of classes of patterns, and then a distance measure is used to measure the separation of the distributions of the features of the two classes.
		Therefore the mutual information compute the difference of the incertitude to which a class pattern belong before the measurement of the feature vector X and after the measurement of X. The mutual information can therefore be used to estimate the quality of a feature set (i.e.
	cgm.cs.mcgill.ca /~soss/cs644/projects/simon (792 words)

	Excessive Candour
		Pattern Recognition is not the first SF novel to tapdance the tympanum of the world in this fashion.
		Pattern Recognition begins in Camden Town, a district of London dominated by markets and venues, a place where the world seems isomorphic with the words that spin it.
		That the pattern is in fact not a city map, that it is in fact something whose implications wrench the heart, the reader will discover.
	www.scifi.com /sfw/issue305/excess.html (1360 words)

	Laboratory Brochure - 17 April 95
		Pattern recognition is the process of grouping or categorizing data based on similar sets of features.
		We are investigating extraction and evaluation of features for recognition of three-dimensional objects and construction of object models.
		The major thrusts of the research are: sensing and feature extraction, modeling of 3-D objects, recognition and pose estimation using matching of object and model features, and integrating mechanical CAD techniques in object modeling.
	www.cse.msu.edu /rgroups/prip/General/brochure (7164 words)

	Review: Pattern recognition and its implications on machine learning (Site not responding. Last check: 2007-11-03)
		Anderson's chapter on pattern recognition describes the cognitive processes that may be occuring in a human.
		Pattern recognition, in the visual sense, is making sense of the visual input from the visual organs - namely the eyes.
		Feature analysis does seem to overcome the problems laid out by template matching, however it does appear a little simplistic to explain human recognition of more complicated objects, for example a cow, or a pair of glasses.
	www.scism.sbu.ac.uk /inmandw/review/ml/review/rev5433.html (1896 words)

	PATTERN RECOGNITION & SENSORY MEMORY
		PATTERN RECOGNITION is the ability to identify objects in the environment--a necessary first step in all cognitive processses.
		2.) "Critical" features, those relationships among features which are most critical to a pattern, can be identified: i.e., for the letter ‘A’ the critical point is that two approximately 45-degree lines intersect as near to the top as possible and the cross bar intersects both lines as nearly as possible, bisecting both lines.
		Since the same features tend to occur in many patterns this would mean a considerable savings in storage.
	home.sandiego.edu /~taylor/pattrec.html (1442 words)

	Pattern Recognition by William Gibson
		Pattern Recognition is set in the present, or perhaps more exactly, the very recent past (relative to January 2003).
		One of the features of this world is that the trademarks, logos and icons of our age, which most of us ignore, are part of the base structure of reality, not just minor color.
		Pattern Recognition is set firmly in the modern world and is full of references that are current in the early part of the twenty-first century.
	www.bearcave.com /bookrev/pattern_recognition.html (1695 words)

	ECE 598NA - Pattern Recognition
		Pattern Recognition is concerned with recognition of an unknown given object as belonging to one of a number of classes.
		Classification is performed by discovering class specific "patterns" among a range of measurable object features and utilizing these class characteristic features for recognition of unknown objects.
		The design of a pattern recognition system requires development of four major modules: sensing, feature extraction, decision making, and system performance evaluation.
	www.ece.uiuc.edu /courses/coursedes.asp?598NA (159 words)

	Pattern Recognition
		"Pattern recognition is the research area that studies the operation and design of systems that recognize patterns in data.
		The recognition and understanding of sensory data like speech or images, which are major concerns in pattern recognition, have always been considered as important subfields of artificial intelligence.
		Methods are developed to sense objects, to discover which of their features distinguish them from others, and to design algorithms which can be used by a machine to do the classification.
	www.aaai.org /AITopics/html/pattern.html (1508 words)

	CISC 859 Pattern Recognition
		To apply statistical pattern recognition, we choose a set of features and characterize the distribution of feature values for each pattern class; we then classify an unknown pattern based on its observed feature values.
		Structural pattern recognition, a newer branch of pattern recognition, constructs descriptions of internal pattern structure.
		Syntactic pattern recognition (one form of structural pattern recognition) uses grammatical techniques to describe and analyze the structure of a pattern.
	www.cs.queensu.ca /home/blostein/859.html (803 words)

	Progress report on pattern recognition (Site not responding. Last check: 2007-11-03)
		A survey of the field of pattern recognition is presented in a manner broad enough not to be limited to the progress in recent years alone.
		Pattern recognition is discussed both intuitively and, more formally, as a many-to-one mapping.
		An essential aspect of pattern recognition, relevant for this classification, is the selection of features by means of preprocessing the input data.
	stacks.iop.org /0034-4885/43/785 (303 words)

	A Statistical Learning/Pattern Recognition Glossary
		This feature extraction technique is closely related to exploratory projection pursuit, commonly used for visualization.
		Basis function regression where each new feature is based on the distance to a prototype, hence the basis is "radial." The resulting curve is a superposition of "bumps," one at each prototype.
		The new features are like basis functions in basis function regression, and the classifier is essentially thresholding a basis function regression.
	www.cs.wisc.edu /~hzhang/glossary.html (4913 words)

	Pattern Recognition Project (Site not responding. Last check: 2007-11-03)
		The first method uses the magnitude of the bins of an FFT as the features and the second one computes the cepstral coefficients by means of a linear spacing of frequency bins.
		The third feature extraction technique is the same as described in [3].
		The mean and variance of each feature is estimated across all training data, then a bias and a gain are applied to each feature so that it has zero mean and unit variance.
	www.cnel.ufl.edu /~hildk/solution.html (970 words)

	Pattern recognition system employing unlike templates to detect objects having distinctive features in a video field ...
		Initially a first template, having a first pattern similar to one of the distinctive features of the object, is passed over the video field and compared to it in order to preliminarily identify at least one possible distinctive feature as a candidate.
		A second template is then created by taking one of the major elements of the distinctive feature candidate and extending that element all the way across the second template and then comparing it to the distinctive feature candidate.
		A third template is then created having a pattern formed from another major element of said distinctive feature and extending it all the way across the third template.
	www.delphion.com /details?&pn=US05627915__ (581 words)

	CS3361: Pattern Recognition (Site not responding. Last check: 2007-11-03)
		Features are used in "Identikit" systems to generate drawings of faces.
		Character recognition: binary sensor decides if pixel is fl or white.
		features: in 10 msec window: overall energy, energy in different frequency ranges, changes of features with respect to previous frame.
	www.cc.gatech.edu /classes/cs3361_98_spring/lecture-1.html (203 words)

	Pattern Recognition
		D. Many variations on a pattern may be recognized as the same ìobjectî or class of objects.
		Recognition is process of comparing the features of the input to the features of prototypes, and selecting the best fit.
		Yin (1970), and Rock (1974) demonstrated that facial recognition is more easily impaired by inversion than is object recognition.
	www.mtsu.edu /~sschmidt/Cognitive/pattern/pattern.html (322 words)

	Pattern Recognition Project (Site not responding. Last check: 2007-11-03)
		They are acoustic-phonetic, statistical pattern recognition and artificial intelligence [1].
		They have generally been regarded as inferior to statistical pattern recognition solutions and so they are not commonly used [1]; however, it should be noted that improvements are being made in the area of acoustic-phonetic speech recognition.
		The most common approach to speech recognition is to use a statistical pattern recognition system [1].
	www.cnel.ufl.edu /~hildk/intro.html (237 words)

	Pattern Recognition Paper (Site not responding. Last check: 2007-11-03)
		The features are subsequently employed by a classificator to classify objects into classes.
		As feature candidates geometrical invariants are often used to classify objects in binary images.
		This back transform from feature space to object space can be used to examine and visualize the class-boundaries through the construction of a "feature-editor" for image features.
	www.isd.uni-stuttgart.de /~rudolph/patternrec/pattern_abstract_07.html (294 words)

	Amazon.com: Pattern Recognition Engineering: Books: Morton Nadler,Eric P. Smith (Site not responding. Last check: 2007-11-03)
		Serves as an introduction to the field of pattern recognition through a unique parallel development of statistical and structural approaches.
		Features comprehensive and critical coverage of edge direction, state machine, nearest neighbor and iterative learning methods.
		Features a general, introductory-level treatment of the basic principles and dominant trends in modern pattern recognition.
	www.amazon.com /exec/obidos/tg/detail/-/0471622931?v=glance (601 words)

	Pattern Recognition (Site not responding. Last check: 2007-11-03)
		Two features namely, the number of closed regions and the existence of curved segments, are sufficient to differentiate the five characters.
		A decision tree can be used to represent the differentiation process as a function of features.
		The features are not appropriate for a real life application, because they are very sensitive to measurement errors.
	www.engr.uconn.edu /~mgrecu/RECOG/HWK/recog-hw1.html (114 words)

	Pattern Recognition Letters (Site not responding. Last check: 2007-11-03)
		Subject: Pattern Recognition Letters Date: Sun, 4 Apr 1999 13:46:46 -0400 (EDT) Pattern Recognition Letters http://www.elsevier.com:80/inca/publications/store/5/0/5/6/1/9/ http://www.elsevier.com/locate/patrec Content available to subscribers at: http://www.sciencedirect.com/science/journal/01678655 ISSN: 0167-8655 AIMS AND SCOPE Pattern Recognition Letters takes a novel approach to the publication of research work in the field of pattern recognition.
		Its main features are: concise articles, rapid publication and a broad coverage.
		The subject matter of Pattern Recognition Letters is image processing and pattern recognition.
	gort.ucsd.edu /newjour/p/msg02556.html (205 words)

	The Pattern Recognition Basis of AI
		The choice of these features is ad-hoc, there is no guarantee that they will work well with the set of characters you want to recognize.
		The vertical and horizontal patterns are found in region 1 of the letter E pattern and there are no diagonals present, code this as the sequence (1,1,0,0).
		To get effective pattern recognition without this technique you would have to list large numbers of vectors, one for each possible way of drawing the E within the 21 x 21 area.
	www.dontveter.com /basisofai/char.html (1036 words)

	BYTE.com (Site not responding. Last check: 2007-11-03)
		Pattern recognition can help you classify and find meaning in masses of data, be it numerical, textual, audio, or video.
		Real-time pattern recognition has been the domain of supercomputers and mainframes because each sample usually requires billions of recognition operations.
		Handwriting recognition is one of a number of applications that depend on accurately classifying data, and classification is SPR's forte.
	www.byte.com /art/9502/sec10/art1.htm (998 words)

	COS 226 Pattern Recognition Assignment (Site not responding. Last check: 2007-11-03)
		Feature detection involves selecting important features of the image; pattern recognition involves discovering patterns in the features.
		This kind of pattern recognition arises in many other applications, for example statistical data analysis.
		Given a set of N feature points in the plane, determine every line segment that contains 4 or more of the points, and plot all such line segments.
	www.cs.princeton.edu /courses/archive/fall04/cos226/assignments/lines.html (453 words)

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