Results 211 to 220 of about 731,110 (244)
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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).
Jean Guibert, Li Wang, Dong-Chen He
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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).
Jean Guibert, Li Wang, Dong-Chen He
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Redundancy in Feature Extraction
IEEE Transactions on Computers, 1971Given 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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1998
Barlow (1961) proposed that the goal of sensory coding is to transform the input signals such that it reduces the redundancy between the inputs. Atick (1992) and Atick and Redlich (1993) have used correlation-based methods suggesting that the principle of redundancy reduction may be applied towards the understanding of coding principles in retinal ...
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Barlow (1961) proposed that the goal of sensory coding is to transform the input signals such that it reduces the redundancy between the inputs. Atick (1992) and Atick and Redlich (1993) have used correlation-based methods suggesting that the principle of redundancy reduction may be applied towards the understanding of coding principles in retinal ...
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Proceedings of the IEEE, 1969
A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented. The main emphasis is on usable techniques applicable to practical image processing systems. The various methods are discussed under the broad headings of microanalysis and macroanalysis.
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A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented. The main emphasis is on usable techniques applicable to practical image processing systems. The various methods are discussed under the broad headings of microanalysis and macroanalysis.
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Feature Extraction and Representation
2018This chapter is focused on some classical feature representations for image and video analysis. In particular, we will introduce the histogram-based features, texture features, and some local point features.
Yonggang Li +5 more
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Modification of strategies in feature extraction
Clinical Linguistics & Phonetics, 1990A training procedure is described which seems appropriate to help severe to profound hearing-impaired subjects to make better use of their residual hearing. The training starts from the assumption that an auditory training is possible, which is based on the enhancement of certain acoustic cues that are still above threshold, but which are normally ...
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2017
Neural feature extraction is an effective method for acquiring quantitative information from neural signals. It is especially important for real–time decision support systems, including closed-loop brain–machine interface systems. The implementation of on-chip energy efficient neural feature extraction significantly reduces the wireless bandwidth ...
Xilin Liu, Jan Van der Spiegel
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Neural feature extraction is an effective method for acquiring quantitative information from neural signals. It is especially important for real–time decision support systems, including closed-loop brain–machine interface systems. The implementation of on-chip energy efficient neural feature extraction significantly reduces the wireless bandwidth ...
Xilin Liu, Jan Van der Spiegel
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2011
In this chapter,\(^\dagger\) we present a deep model-based and data-driven hybrid architecture (DMD) for feature extraction. First, we construct a deep learning pipeline for progressively learning image features from simple to complex. We mix this deep model-based pipeline with a data-driven pipeline, which extracts features from a large collection of ...
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In this chapter,\(^\dagger\) we present a deep model-based and data-driven hybrid architecture (DMD) for feature extraction. First, we construct a deep learning pipeline for progressively learning image features from simple to complex. We mix this deep model-based pipeline with a data-driven pipeline, which extracts features from a large collection of ...
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Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Longlong Jing, Yingli Tian
exaly
Feature Selection and Extraction
2010Conventional classifiers do not have a mechanism to control class boundaries. Thus if the number of features, i.e., input variables, is large compared to the number of training data, class boundaries may not overlap.
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