Results 231 to 240 of about 2,887,511 (291)

PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion. [PDF]

Front Med (Lausanne)
Zhang S, Tang R, Mu J, Ren S.
europepmc   +1 more source

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization. [PDF]

PeerJ Comput Sci
S SV, P J.
europepmc   +1 more source

A Study of Non-Linear Manifold Feature Extraction in Spike Sorting. [PDF]

Neuroinformatics
Ardelean ER, Portase R.
europepmc   +1 more source

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Texture feature extraction

Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He
exaly   +2 more sources

Systematic Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A systematic feature extraction procedure is proposed. It is based on successive extractions of features. At each stage a dimensionality reduction is made and a new feature is extracted. A specific example is given using the Gaussian minus-log-likelihood ratio as a basis for the extracted features.
Kenneth A. Brakke, James M. Mantock, Keinosuke Fukunaga   +2 more
openaire   +3 more sources

Feature Selection and Feature Extraction: Highlights

Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2021
In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction.
Hiu-Man Wong, Xingjian Chen, Hiu-Hin Tam, Jiecong Lin, Shixiong Zhang 0002, Shankai Yan, Xiangtao Li, Ka-Chun Wong   +7 more
openaire   +1 more source

Feature Extraction

2016
Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti, A Cerri, G Patanè, M Spagnuolo   +3 more
openaire   +2 more sources

Feature extraction in the Neocognitron

IEEE International Conference on Neural Networks, 1988
The authors present theoretical and numerical developments in the understanding of feature extraction in the Neocognitron. First, they show that the feature extraction process is equivalent to a generalized nonlinear discriminant. Second, they show that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of
Ken Johnson, Cindy Daniell, Jerry Burman
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Feature Extraction

2012
In the previous chapters we have examined static and dynamic methods of program analysis. These features must be translated into mathematical representations and birthmarks to be useful. Furthermore, mathematical representations may be embedded in other mathematical types to make birthmarks more amenable to similarity comparisons and for use in ...
Cesare, S, Xiang, Yang
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Redundancy in Feature Extraction

IEEE Transactions on Computers, 1971
Given two random variables X and Y, a definition is offered that gives a condition for Y to be redundant with respect to X. It is shown that if such redundancy exists, then observations on Y, i.e., pattern vector elements related to Y, can be eliminated without increasing the classification error.
openaire   +3 more sources