Results 121 to 130 of about 427,913 (167)

Coupled-Feature Hypergraph Representation for Feature Selection

2015
Real-world objects and their features tend to exhibit multiple relationships rather than simple pairwise ones, and as a result basic graph representation can lead to substantial loss of information. Hypergraph representations, on the other hand, allow vertices to be multiply connected by hyperedges and can hence capture multiple or higher order ...
Zhihong Zhang, Jianbing Xiahou, Lu Bai, Edwin R. Hancock   +3 more
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Grammatical Feature Representation

2002
How we refer to a given inflectional form may depend on the perspective of the feature opposition. For instance, consider the verb iku ‘go’, the adjective hurui ‘is old’ and the copula da. Stylistically, these forms are the plain forms as opposed to the polite versions that contain a polite morpheme-(i)mas- or -es-: Open image in new ...
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Autocorrelation-Based Features for Speech Representation

Acta Acustica united with Acustica, 2013
This study investigates autocorrelation-based features as a potential basis for phonetic and syllabic distinctions. The work comes out of a theory of auditory signal processing based on central monaural autocorrelation and binaural crosscorrelation representations.
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3D Point Feature Representations

2013
In their native representation, points as defined in the concept of 3D mapping systems are simply represented using their Cartesian coordinates x, y, z, with respect to a given origin. Assuming that the origin of the coordinate system does not change over time, there could be two points p 1 and p 2, acquired at t 1 and t 2, having the same coordinates.
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Feature Representation and Learning

2015
In this chapter, first, some basics concepts about feature extraction and how to use sparse coding for feature representation and dimension reduction are detailed. Then it gives the concepts of dictionary learning methods including K-SVD, discriminative dictionary learning, online dictionary learning, supervised dictionary learning, and joint ...
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Feature Extraction: Issues, New Features, and Symbolic Representation

1999
Feature extraction is an important part of object model acquisition and object recognition systems. Global features describing properties of whole objects,or local features denoting the constituent parts of objects and their relationships may be used. When a model acquisition or object recognition system requires symbolic input,the features should be ...
Maziar Palhang, Arcot Sowmya
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Boundary Representation-based Feature Identification

1994
Abstract This chapter describes the automated recognition of form features from boundary representations of solid models. The methods covered include rule-based, graph-based and neural net based techniques. Each method is briefly described with specific instances of each referenced and pros and cons listed.
Mark R. Henderson, Gopal Srinath, Roger Stage, Kim Walker, William Regli   +4 more
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Local Representation of Facial Features

2011
Feature extraction is one of the fundamental tasks in computer vision and image processing. Respectively, the task of selecting the best set of features to describe faces for recognition, verification, localization, or detection, is a fundamental problem in face biometrics.
Joni-Kristian Kämäräinen, Abdenour Hadid, Matti Pietikäinen   +2 more
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Constructing featural representations

2007
The phonology ...
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“Features for shape representation”

1998
Shape information provides useful representations for an object, often sufficient for its recognition. In particular, shape information can be found at points where there is a change in an otherwise continuous gradient or where the direction of the contour changes more rapidly. Moreover, also symmetry plays an important role.
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