Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics

2010 ◽  
Vol 37 (10) ◽  
pp. 7205-7212 ◽  
Author(s):  
Mahmoud Omid ◽  
Asghar Mahmoudi ◽  
Mohammad H. Omid
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sorting System

scholarly journals A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

2015 ◽  
Vol 7 (3) ◽  
pp. 11-19 ◽  
Author(s):  
M. Z. Uddin ◽  
M. A. Yousuf
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Human Action ◽  
Human Posture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.

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