Results 211 to 220 of about 500,210 (263)

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

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
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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, Li Wang 0002, Jean Guibert
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A feature fusion method for feature extraction

SPIE Proceedings, 2012
The automatic target recognition based on image fusion refers to the fusion process using the target images provided by a variety of sensors, so as to improve the recognition accuracy and robustness and to obtain better recognition performance.
Dejun Tang, Weishi Zhang, Xiaolu Qu, Dujuan Wang   +3 more
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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.
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Pronunciation Feature Extraction

2005
Automatic pronunciation scoring makes novel applications for computer assisted language learning possible. In this paper we concentrate on the feature extraction. A relatively large feature vector with 28 sentence- and 33 word-level features has been designed.
Christian Hacker, Tobias Cincarek, Rainer Gruhn, Stefan Steidl, Elmar Nöth, Heinrich Niemann   +5 more
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Feature Extraction Through LOCOCODE

Neural Computation, 1999
Low-complexity coding and decoding (LOCOCODE) is a novel approach to sensory coding and unsupervised learning. Unlike previous methods, it explicitly takes into account the information-theoretic complexity of the code generator. It computes lococodes that convey information about the input data and can be computed and decoded by low-complexity ...
Hochreiter, Sepp, Schmidhuber, Jürgen
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On Feature Extraction via Kernels

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be employed to extract nonlinear features. In this correspondence, we study the relationship between the two kernel ideas applied to certain feature extraction algorithms such as linear ...
Cheng Yang, Liwei Wang 0001, Jufu Feng
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Feature extraction algorithms

Pattern Recognition, 1971
Abstract This paper describes methods for extracting pattern-synthesizing features. A set of patterns is expressed as a Boolean matrix, allowing the problem of feature extraction to be viewed as one of factoring this matrix. Feature extraction methods based on matrix factorization and pattern intersection are presented. Attribute inclusion is defined
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Features extraction for speech emotion

Journal of Computational Methods in Sciences and Engineering, 2009
In this paper the speech emotion verification using two most popular methods in speech processing and analysis based on the Mel-Frequency Cepstral Coefficient (MFCC) and the Gaussian Mixture Model (GMM) were proposed and analyzed. In both cases, features for the speech emotion were extracted using the Short Time Fourier Transform (STFT) and Short Time ...
Norhaslinda Kamaruddin, Abdul Wahab 0001
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