Study on a Rapid Real-Time Feature Extraction Algorithm

2013 ◽  
Vol 709 ◽  
pp. 575-578
Author(s):  
Bin Xu ◽  
Xiang Na Li ◽  
Wei Ning Xue
Keyword(s):  
Feature Extraction ◽  
Target Position ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Region Detection ◽  
Centre Of Gravity ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Object Region

A fast feature extraction algorithm is presented in this paper based on the color and point feature, With the aim of required feature points, the location of the object includes object region detection and feature point location. The area of object detection is used to look for the centre of gravity point from the scaling image. The feature point location is based on the object region detection, cuts a picture from the image, and extracts speeded up robust feature (SURF) points of the target within the cut picture. The target position is calculated according to the value of the feature points, it provides a basis for object tracking. The experimental results verify the effectiveness of the proposed method.

Study of Fingerprint Image Feature Extraction Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 577-580
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Image Feature ◽  
Feature Points ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Image Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Fingerprint image feature extraction is a critical step to fingerprint recognition system, which studies topological structure, mathematical model and extraction algorithm of fingerprint feature. This paper presents system design and realization of feature extraction algorithm for fingerprint image. On the basis of fingerprint skeleton image, feature points including ending points, bifurcation points and singular points are extracted at first. Then false feature points are detected and eliminated by the violent changes of ambient orientation field. True feature points are marked at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

The Application of Cumulative Sum Feature Extraction Algorithm in Data Stream Anomaly Analysis before Earthquake

2013 ◽  
Vol 765-767 ◽  
pp. 524-527
Author(s):  
Li Ping Chen ◽  
Xiang Zeng Kong ◽  
Pan Xiong ◽  
Jing Zeng ◽  
Xin Qi Lin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comparative Study ◽  
Data Stream ◽  
Feature Points ◽  
Cumulative Sum ◽  
Seismic Precursors ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Anomaly Analysis ◽  
Eastern Japan

A cumulative sum feature extraction algorithm for anomaly analysis before earthquake is proposed by using the cumulative sum feature extraction algorithm. The analysis results reveal that the anomalous variations exist prior to the earthquakes. The methods studied in this work include cumulative sum, smooth using the Technology of Locally weighted smoothing and compute feature points. These methods have been applied to analyze the anomaly from OLR data that correspond to seismic precursors, particularly to a comparative study of the two earthquakes of Wenchuan in China and one earthquake in eastern Japan.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Visible Images

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

2012 ◽  
Vol 19 (10) ◽  
pp. 639-642 ◽  
Author(s):  
Qianwei Zhou ◽  
Guanjun Tong ◽  
Dongfeng Xie ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Feature Extraction ◽  
Target Classification ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Ground Target

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Sign in / Sign up

Export Citation Format

Share Document