scholarly journals Fast Polar and Spherical Fourier Descriptors for Feature Extraction

2010 ◽  
Vol E93-D (7) ◽  
pp. 1708-1715 ◽  
Author(s):  
Zhuo YANG ◽  
Sei-ichiro KAMATA
Keyword(s):  
Feature Extraction ◽  
Fourier Descriptors

scholarly journals Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors

2017 ◽  
Vol 7 (4) ◽  
pp. 1334
Author(s):  
Anis Azwani Muhd Suberi ◽  
Wan Nurshazwani Wan Zakaria ◽  
Razali Tomari ◽  
Nurmiza Othman ◽  
Nik Farhan Nik Fuad
Keyword(s):  
Dendritic Cells ◽  
Feature Extraction ◽  
Fourier Descriptors ◽  
Geometric Features

scholarly journals Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yunhua Yu ◽  
Haitao Shi ◽  
Lifei Mi
Keyword(s):  
Feature Extraction ◽  
Working Condition ◽  
Fourier Descriptors ◽  
Gray Level ◽  
Memory Space ◽  
Geometric Moment ◽  
Moment Vector ◽  
Sucker Rod ◽  
Sucker Rod Pump ◽  
Condition Diagnosis

Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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