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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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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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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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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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2019
This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FD.
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This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FD.
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On Feature Extraction via Kernels
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008Using 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 ...
Jufu Feng, Cheng Yang, Liwei Wang
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2019
This chapter focuses on one of the three major types of image features; colors. It first gives a brief introduction to color science, followed by the introduction of four color spaces commonly used in image feature extraction. Readers are demonstrated with pros and cons of each color space.
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This chapter focuses on one of the three major types of image features; colors. It first gives a brief introduction to color science, followed by the introduction of four color spaces commonly used in image feature extraction. Readers are demonstrated with pros and cons of each color space.
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2009
This chapter presents a brief review of the previous work on the related topics of feature representation and recognitions. The first section describes previous research efforts in the area of feature representation. Previous research in the area of feature recognition is described in the second section.
Emad Abouel Nasr +3 more
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This chapter presents a brief review of the previous work on the related topics of feature representation and recognitions. The first section describes previous research efforts in the area of feature representation. Previous research in the area of feature recognition is described in the second section.
Emad Abouel Nasr +3 more
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1975
Publisher Summary This chapter discusses the feature extraction. Vector and grammatical classification methods presuppose the existence of features that can be measured. Finding the features is often a considerable step toward solving the problem. There are two general ways to approach feature analysis, depending upon the assumptions one wants to make
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Publisher Summary This chapter discusses the feature extraction. Vector and grammatical classification methods presuppose the existence of features that can be measured. Finding the features is often a considerable step toward solving the problem. There are two general ways to approach feature analysis, depending upon the assumptions one wants to make
openaire +2 more sources
Pronunciation Feature Extraction
2005Automatic 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.
Rainer Gruhn +5 more
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