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Feature extraction for hyperspectral image classification: a review
International Journal of Remote Sensing, 2020Hyperspectral image sensors capture surface reflectance over a range of wavelengths. The fine spectral information is recorded in terms of hundreds of bands.
B. Kumar +3 more
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Informative Feature Extraction
2021Laser molecular imaging produces high-dimension data with the structure dependent on the optical modality, laser type, detection method, kind of sample, etc. Generally, data’s high dimension corresponds to a situation where the number of initial parameters exceeds by orders of magnitude the number of hidden independent variables, e.g., when the number ...
Alexey V. Borisov +2 more
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Feature extraction in the Neocognitron
IEEE International Conference on Neural Networks, 1988The 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
Burman, Johnson, Daniell
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2015
This chapter extensively discusses the dataset preprocessing techniques and how the features are extracted from the dataset. It started off with an introductory chapter that also covers the structural overview of the sections in this chapter. The next section discusses the processing techniques introduced on the dataset followed by the feature ...
Oluwatobi Ayodeji Akanbi +2 more
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This chapter extensively discusses the dataset preprocessing techniques and how the features are extracted from the dataset. It started off with an introductory chapter that also covers the structural overview of the sections in this chapter. The next section discusses the processing techniques introduced on the dataset followed by the feature ...
Oluwatobi Ayodeji Akanbi +2 more
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2000
Publisher Summary This chapter proposes a methodology used to extract salient features from a tensor map produced after a voting process, by formulating the problem as a level-crossing detection problem, which can then be solved with a modified version of Lorensen and Cline's marching cubes algorithm.
Chi-Keung Tang +2 more
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Publisher Summary This chapter proposes a methodology used to extract salient features from a tensor map produced after a voting process, by formulating the problem as a level-crossing detection problem, which can then be solved with a modified version of Lorensen and Cline's marching cubes algorithm.
Chi-Keung Tang +2 more
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2011
In the last chapter we discussed the acquisition and processing of images. We learned that images are simply large arrays of pixel values but for robotic applications images have too much data and not enough information. We need to be able to answer pithy questions such as what is the pose of the object? what type of object is it? how fast is it moving?
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In the last chapter we discussed the acquisition and processing of images. We learned that images are simply large arrays of pixel values but for robotic applications images have too much data and not enough information. We need to be able to answer pithy questions such as what is the pose of the object? what type of object is it? how fast is it moving?
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Feature Extraction and Selection
2012The computational complexity of a classification algorithm should be reduced to a sufficient minimum by reducing the number of features considered. We can either select the most informative features or extract a new, smaller set of features using a (linear) combination of the original features.
Derek Abbott +2 more
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2003
In this paper we present a method for automatic extraction of shape features, called crest lines. Shape features are important because they provide an alternative to describing an object, using its most important characteristics and reduce the amount of information stored.
Gerald Farin, Georgios Stylianou
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In this paper we present a method for automatic extraction of shape features, called crest lines. Shape features are important because they provide an alternative to describing an object, using its most important characteristics and reduce the amount of information stored.
Gerald Farin, Georgios Stylianou
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1994
In the preceding chapters, emphasis was put on the use of MLPs as discriminant pattern classifiers for speech recognition applications. Although pattern classification plays a crucial role, it is only part of the vast speech recognition task. In spite of the spectacular progress made over the last decade, unrestricted speech recognition is still out of
Hervé Bourlard +2 more
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In the preceding chapters, emphasis was put on the use of MLPs as discriminant pattern classifiers for speech recognition applications. Although pattern classification plays a crucial role, it is only part of the vast speech recognition task. In spite of the spectacular progress made over the last decade, unrestricted speech recognition is still out of
Hervé Bourlard +2 more
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Plaque Feature Extraction [PDF]
, 2011 Feature extraction is a critical step in any pattern classification system. In order for the pattern recognition process to be tractable, it is necessary to convert patterns into features, which are condensed representations of the patterns, containing only salient information.
Efthyvoulos Kyriacou +3 more
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