Research on digital image recognition system based on multiple invariant moments theory and BP neural network

2010 ◽  
Author(s):  
Han Jian-ning ◽  
Wang Ming-quan
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Digital Image ◽  
Bp Neural Network ◽  
Recognition System ◽  
Invariant Moments

Study on Rotary Object Recognition Technique from the Complex Background

2011 ◽  
Vol 418-420 ◽  
pp. 494-500
Author(s):  
Bao Zhang Li ◽  
Mo Yu Sha ◽  
Yan Ping Cui
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Bp Neural Network ◽  
Target Recognition ◽  
Median Filter ◽  
Recognition System ◽  
Invariant Moments ◽  
Complex Background ◽  
Recognition Result ◽  
The Military

Target recognition from complex background is the emphasis and difficulty of computer vision, and rotary objects is widely used in the military and manufacturing field. Rotary object recognition in complex background based on improved BP neural network is proposed in the dissertation. Median filter is adopted to get rid of the noise and an improved method of maximum classes square error is used to compute the threshold of the image segmentation. The target recognition system based on improved BP neural network is established to recognize the rotary objects, and seven invariant moments of rotary objects serve as the input feature vector. The experiment results show that the image noise could be gotten rid of effectively and the image could be segmented exactly by the image preprocessing method put forward in the dissertation, and the seven invariant moments is appropriate for the character of rotary objects, and the rotary object recognition system based on BP neural network acquires an excellent recognition result.

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

Character Image Recognition Based on the Improved BP Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 2655-2658 ◽  
Author(s):  
Hong Zhao
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Simulation Results ◽  
Improved Bp Algorithm

This paper raises a kind of improved BP algorithm in order to compensate for some shortcomings which exist in traditional BP neural network. It has been applied to the recognition of character images. Computer simulation results demonstrate that it does bring about an ideal result.

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