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Topic: Feature extraction

Related Topics

Hough transform

Optical flow

Digital image processing

Wavelet

Medical image processing

Algorithm

Liberal Party (UK)

John Lubbock

	Feature Extraction Using Spatial Context
		Features extracted from an image populate a GIS database and support decision-makers for a wide variety of applications such as land-use planning, disaster and emergency services, telecommunications, etc. Image classifiers can also be used to extract from imagery some types of specific objects or targets, such as land-cover types using multispectral imagery (MSI).
		The idea behind this approach is that, with a sample of extracted features from the image, a learning algorithm automatically develops a model that correlates known data (such as pixel values from images, terrain data, vector overlays, grids etc.) with targeted features.
		With feature extraction, the difficulty is including enough spatial information without overwhelming the learner.
	gis.esri.com /library/userconf/proc02/pap0813/p0813.htm (820 words)

	Features (Site not responding. Last check: 2007-10-21)
		Our approach uses the surfaces of the model itself to split the features from the base shape, which we call the “body.” In the case of Figure 1, for instance, the top face of the large box can be used split the model and isolate the protrusion.
		Often small, unimportant features cause the mesh to be unnecessarily fine, resulting in an unnecessarily large stiffness matrix, and thus unnecessary computation.
		At this point, both the feature and body models are incomplete; Both contain a “hole.” To complete the extraction of the feature, we use pieces of the split edges to close the hole in the part.
	www.me.cmu.edu /faculty1/stahovich/smarttools2/research/features.htm (1159 words)

	Applying Neural Networks to Character Recognition (Site not responding. Last check: 2007-10-21)
		The idea of the feature point extraction algorithm is to identify characters based on features that are somewhat similar to the features humans use to identify characters.
		Comparisons were executed by computing the sum of the minimum distances between the feature points of the character to be identified and the feature points of the dictionary character.
		By keeping the first step separate, the preprocessing code from the feature extraction OCR program could be used, eliminating this one area of difference between the two algorithms.
	www.ccs.neu.edu /home/feneric/charrecnn.html (1616 words)

	Wildlife Spatial Analysis Lab (Site not responding. Last check: 2007-10-21)
		Many of the features they contain, such as hydrography networks, are potentially useful as data inputs to wildlife models, but are not in usable digital form.
		However, these data are two dimensional and features are not easily separable from one another except using human perceptions of connectivity and association.
		Our extraction process requires two stages: the automated stage, in which all tasks are performed by the computer, and the manual stage, where finishing edits are performed by an analyst.
	www.wru.umt.edu /project/hydro (1099 words)

	Hierarchical Feature Extraction: Removing the Clutter
		It is a difficult task to identify all the nuances of a complex feature in a single classification.
		The goal of the Feature Analyst's hierarchical feature extraction is to leverage a human's impressive vision ability to improve classification results by mitigating clutter (false positives), and retrieving missed objects.
		Feature Analyst begins the hierarchical process the same as we approach any baseline inductive learning classification, i.e., select labeled examples for the feature being extracted, train the learner, and then classify every pixel in the image based on the learner's prediction.
	gis.esri.com /library/userconf/proc02/pap0924/p0924.htm (1155 words)

	BioMedical Engineering OnLine \| Abstract \| Feature extraction for the analysis of colon status from the endoscopic ...
		The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status.
		Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images.
		Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal.
	www.biomedical-engineering-online.com /content/2/1/9/abstract (301 words)

	Feature Extraction
		A total of eleven features were used to differentiate affective state: the mean EMG activity, the mean and mean slope of the skin conductivity, average heart rate and heart rate change, and the normalized mean, variance, and four power spectral density characteristics of the respiration signal.
		The features (RF1-RF4) represent the average energy in each of the first four 0.5Hz bands of the PSD (0-2Hz).
		The physiological features extracted were hypothesized to reflect the physiological changes in the person experiencing the emotion.
	vismod.media.mit.edu /tech-reports/TR-444/node3.html (717 words)

	BioMedical Engineering OnLine \| Full text \| Feature extraction for the analysis of colon status from the endoscopic ...
		Features based on texture analysis were derived using the co-occurrence matrix, viz., angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation [2].
		Features from texture and color are extracted from the endoscopic image to identify a normal colon from an abnormal colon.
		This does not pose a problem to the feature extraction module, as the main objective behind the parameter extraction was to differentiate the normal condition from the abnormal condition.
	www.biomedical-engineering-online.com /content/2/1/9 (3923 words)

	TechLink : Software & Information Technologies : The Feature Analyst ™ Extension for ArcView ®: Automated Feature ...
		The Feature Analyst software provides the GIS community with a paradigm shift in feature extraction technology using spatial context and spectral signature to automatically extract user-defined objects from aerial and satellite imagery.
		Geographic features, such as streets, buildings, vegetation, etc. are used in a GIS to produce maps and perform spatial analyses for planning, transportation analyses, defense, telecommunications, and many other applications.
		The Feature Analyst is designed to significantly lower the cost of extracting cartographic features from panchromatic and multi-band imagery and is the most robust and accurate feature extraction software on the market.
	www.techlinkcenter.org /cgi-bin/techlink/i00702 (564 words)

	3D Feature Extraction for Unstructured Grids
		This process involves searching and analyzing the data for features or effects: objects or structures which have space-time coherence so they are identifiable and have a finite lifetime (possibly a coherent region of high/low intensity).
		(Specific features include vortex cores [4,5, 6], and critical points for vector fields [7], etc.) A basic definition of a feature is a region of interest consisting of voxels satisfying a set of pre-defined criteria.
		The feature extraction algorithm has been extended to work with unstructured grids (with tetrahedral cells) 15.
	www.caip.rutgers.edu /~silver/nasa/nasa.html (2007 words)

	Biologically inspired feature extraction, sensory information fusion and perception methods
		The main goal of this project is to develop and apply methods for feature extraction and systems for sensory information fusion, artificial perception to be used in industrial applications oriented especially to the food industry and intelligent rescue systems (IRS).
		Until now the traditional analysis techniques (PCA, median filter, etc.) has been mainly used for feature extraction requirements and a number of combination of artificial neural networks and fuzzy logic based models have been applied for the fusion of the sensory information originating from two (taste-smell and auditory-visual) sensory devices.
		In the current state of the project a hybrid ANN structure is being tested to handle the sensory outputs of the feature extraction phase based on wavelet transform techniques.
	aass.oru.se /Research/Sensors/proj2.html (259 words)

	Generic Edge Feature Extraction Based on Perceptual Curve Partitioning
		In computer vision, a feature is a locally detectable pattern of pixels from an image which may represent a piece of higher-level information about the image.
		Edge feature extraction is always an initial step for numerous image understanding applications.
		The conventional curve feature extraction methods rely heavily on the precise calculation of curve equation parameters or curvatures, which tend to be computationally intensive and less robust in terms of handling noises and curve shape distortions.
	www.cs.dal.ca /news/def-1217.shtml (295 words)

	What Is Importance-Based Feature Extraction?
		Importance-based feature extraction aims to tune the agent's feature detectors to be most sensitive to states where the agent's choice of action is critical.
		This is an example of importance-based feature extraction, since we are "tuning" our "feature detectors" to respond to those features which make a difference in the things we have to do, and otherwise falling back on broad stereotypes.
		Importance-based feature extraction attempts to tune the feature detectors according to their importance in selecting the agent's actions; a detector is considered important if the links from it to the outputs have very different weights.
	www.cs.wisc.edu /~finton/ibfe.html (1345 words)

	Sociable machines - Low level feature extraction
		Kismet's low-level visual perception system extracts a number of features that human infants seem to be particularly responsive toward.
		These low-level features were selected for their ability to help Kismet distinguish social stimuli (i.e.
		Kismet's low-level auditory perception system extracts a number of features that are also useful for distinguishing people from other sound emitting objects such as rattles, bells, and so forth.
	www.ai.mit.edu /projects/sociable/low-level-features.html (1039 words)

	BioMed Central \| Abstract \| Feature extraction and signal processing for nylon DNA microarrays
		Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels.
		We explore sources of variability in feature extraction from DNA microarrays on Nylon membrane with radioactive detection.
		Our novel feature extraction methodology, based on a mathematical model of the radioactive emission, reduces variability due to saturation, neighbourhood effects and variable probe amount.
	www.biomedcentral.com /1471-2164/5/38/abstract (230 words)

	Low Level Feature Extraction
		The traditional approach to labelling low-level image features is to apply a filter to the grey level image and set a hard threshold on the output of this filter.
		This talk will describe a generalised approach to image feature detection which seeks to label pixels with the conditional probability of the feature occurring at that location and will be illustrated with results from edge and corner detection.
		The feature labelling problem has been recast as one in statistical pattern recognition where we have implemented the classifier with a neural network.
	www.bmva.ac.uk /meetings/meetings/old/feature-meeting.html (1572 words)

	Feature extraction (Site not responding. Last check: 2007-10-21)
		However, a middle ground is achievable by identifying appropriate image features and representing them in a form to enable responses to similar queries by defining areas in the segmented structures and assigning meaning to the measurements on those.
		It is thus important to identify the image features to be extracted to enable responses to queries relevant to the class of images in question.
		The practical use of this system is likely to concentrate on specific features on the vertebra(e) and it is unclear if conventional shape indexing methods are able to support such localized queries.
	archive.nlm.nih.gov /pubs/reports/bosc02/node23.html (1702 words)

	Datasets generated by computer simulations and experiments in Computational Fluid Dynamics and Finite Element Analysis ...
		Feature Extraction and Tracking is a big part of this process.
		Once features are identified, properties of the features and their evolutionary history can be computed.
		Work on distributed feature extraction includes an MPI implementation of the feature extraction, an MPI version of the feature tracking, and a distributed environment with GraCE.
	www.caip.rutgers.edu /vizlab_group_files/RESEARCH/Featuretrackingmain.htm (505 words)

	LAND INFO Worldwide Mapping - vector feature extraction
		Digitizing features is one of the most time consuming and tedious aspect of compiling GIS data, but also one of the most vital ingredients in creating your GIS database.
		Feature Vector Extraction has several areas that need to be checked to assure a quality GIS Data product.
		Scales for the Vector Feature extraction will replicate the scale of the map for which the features were derived.
	www.landinfo.com /products_vectors.htm (428 words)

	DSP Techniques
		However, because of the large variability of the speech signal, it is a good idea to perform some form of feature extraction that would reduce that variability.
		In particular, computing the envelope of the short-term spectrum reduces the variability significantly by smoothing the detailed spectrum, thus eliminating various source information, such as whether the sound is voiced or fricated and, if voiced, it eliminates the effect of the periodicity or pitch.
		So, in the feature extraction, it is very common to perform a frequency warping of the frequency axis after the spectral computation.
	cslu.cse.ogi.edu /HLTsurvey/ch11node5.html (1240 words)

	Feature Extraction (Site not responding. Last check: 2007-10-21)
		Feature extraction is the process of locating and tracking an object - for gait analysis this may be a person's legs, torso or arms and so on.
		The approach I have taken in my project is model-based, which means that each feature is approximated by a mathematical equation.
		Tracking the feature as the person moves is not quite as complicated as you might think.
	www.ecs.soton.ac.uk /~dkw02r/project/features.htm (384 words)

	2000 Poster Workshop: Minutia Verification and Classification for Fingerprint Matching (Site not responding. Last check: 2007-10-21)
		For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and matching is conventionally adopted where each stage transforms a particular component of information relatively independently.
		We propose a feedback path for the feature extraction stage, followed by a feature refinement stage for improving the matching performance.
		A combination of feedback (minutia verification) in the feature extraction phase and feature refinement (minutia classification) improves the overall performance of the fingerprint verification system by ~8%.
	www.cse.msu.edu /Poster2000/G28-prabhaka.html (205 words)

	Automatic Planimetric Feature Extraction based on Model-Based Image Analysis, MBIA from simulated images
		For this purpose, feature extraction and the processing of the available remotely sensed data are major tools.
		In this paper an automatic model-based approach for planimetric feature extraction is presented.
		A typical feature of the method is the separation of the complex combined probabilities of multi-spectral data, object class, object geometry and sensing into a radiometric model and a geometric model.
	www.gisdevelopment.net /technology/ip/mi03008abs.htm (526 words)

	nips2003 challenge on feature extraction instructions
		If no feature set is provided, it will be assumed that all the features were used.
		A certain number of features meaningless by design were introduced in the data (random probes).
		The organizers may also provide the participant with one or several additional test sets containing only the features they selected to verify the accuracy of their classifier when it uses only those features.
	clopinet.com /isabelle/Projects/NIPS2003/FAQ.html (2521 words)

	Feature Extraction Encyclopedia Article, Definition, History, Biography (Site not responding. Last check: 2007-10-21)
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	www.stardustmemories.com /encyclopedia/Feature_extraction (230 words)

	Interactive Point Based Isosurface Extraction - Feature Story - Scientific Computing and Imaging Institute
		The point-based approach is based on an extraction scheme that classifies different sections of the isosurface into four categories.
		Isosurface extraction is an important technique for visualizing three-dimensional scalar fields.
		This research presents a point-based view-dependent isosurface extraction technique that permits the isosurfaces of data set, fitting in the memory of a desktop computer, to extract and render at interactive frame rates.
	www.sci.utah.edu /stories/2004/sum_isosurface.html (472 words)

	Feature extraction (Site not responding. Last check: 2007-10-21)
		Feature extraction is an area of image processing which involves using algorithms to detect and isolate various desired of a digitized image or video stream.
		Feature Extraction, Construction and Selection: A Data Mining Perspective (Kluwer International Series in Engineering and Computer Science, 453)
		Feature extraction and classification algorithms for high dimensional data (SuDoc NAS 1.26:194298)
	www.freeglossary.com /Feature_extraction (148 words)

	Feature Extraction and Tracking: (Site not responding. Last check: 2007-10-21)
		The motion analysis approached used in my doctoral work was based on tracking point features.
		These point features were extracted using a method developed by Prof.
		I developed a method for tracking these point features using labelled graph matching embedded in a recursive filtering framework.
	www.cfar.umd.edu /%7Eshekhar/featext.htm (54 words)

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