Research on the data extraction method of lip shape feature based on facial motion capture technology

2021 ◽  
Author(s):  
Shengyin Zhu ◽  
Zhen Wu
Keyword(s):  
Motion Capture ◽  
Extraction Method ◽  
Data Extraction ◽  
Shape Feature ◽  
Facial Motion ◽  
Feature Based

Data Extraction Method from Printed Images with Different Formats

2017 ◽  
Vol E100.A (11) ◽  
pp. 2355-2357
Author(s):  
Mitsuji MUNEYASU ◽  
Nayuta JINDA ◽  
Yuuya MORITANI ◽  
Soh YOSHIDA
Keyword(s):  
Extraction Method ◽  
Data Extraction

scholarly journals Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids

ELKHA ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 54
Author(s):  
Eska Rizqi Naufal ◽  
Gigih Priyandoko ◽  
Fachrudin Hunaini
Keyword(s):  
Induction Motor ◽  
Extraction Method ◽  
Data Extraction ◽  
Working Environment ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Group Data ◽  
Low Pass

The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%.

Optimization of Image Zernike Moments Shape Feature Based on Evolutionary Computation

2013 ◽  
pp. 199-216
Author(s):  
Maofu Liu ◽  
Huijun Hu
Keyword(s):  
Image Analysis ◽  
Evolutionary Computation ◽  
High Dimension ◽  
Optimization Algorithm ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature ◽  
Original Image ◽  
Feature Based ◽  
Image Shape

The image shape feature can be described by the image Zernike moments. In this chapter, the authors point out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. Therefore, the optimization algorithm based on evolutionary computation is designed and implemented in this chapter to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

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