Results 331 to 340 of about 3,899,370 (385)
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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
openaire +2 more sources
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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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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Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features.
Lunke Fei +4 more
semanticscholar +1 more source
Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2019The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with ...
Nanjun He +6 more
semanticscholar +1 more source
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
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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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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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Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN
International Conference Computing Methodologies and Communication, 2019Agricultural productivity plays a major role in an Indian economy; therefore the disease detection in the field of agriculture is important. Farmers struggle a lot for proper crop production due to multiple diseases affecting the plant so there is a need
C. Kumari, S. Jeevan Prasad, G. Mounika
semanticscholar +1 more source
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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