scholarly journals Image Data Compression and Noisy Channel Error Correction Using Deep Neural Network

2016 ◽  
Vol 95 ◽  
pp. 145-152 ◽  
Author(s):  
Yijing Z. Watkins ◽  
Mohammad R. Sayeh
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Error Correction ◽  
Deep Neural Network ◽  
Image Data ◽  
Noisy Channel ◽  
Channel Error ◽  
Image Data Compression

Study on image data compression by using neural network

1992 ◽  
Author(s):  
Zhong Zheng ◽  
Masayuki Nakajima ◽  
Takeshi Agui
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Image Data ◽  
Image Data Compression

Ceramic powder characterization by multilayer perceptron (MLP) data compression and classification

1984 ◽  
Vol 5 (3) ◽  
pp. 225-239
Author(s):  
Bonifazi G. ◽  
Burrascano P.
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Ceramic Powder ◽  
Morphological Characteristics ◽  
Image Data ◽  
Training Set ◽  
Neural Network Approach ◽  
Image Data Compression ◽  
Testing Patterns ◽  
Compressed Data

A neural network approach for pattern classification has been explored in the present paper as part of the recent resurgence of interest in this area. Our research has focused on how a multilayer feedforward structure performs in the particular problem of particle characterization. The proposed procedure, after suitable data preprocessing, consists of two distinct phases: in the former, a feedforward neural network is used to obtain an image data compression. In the latter, a neural classifier is trained on the compressed data. All the tests have been conducted on a sample constituted by two different typologies of ceramic particles, each characterized by a different microstructure. The sample image of different particles acquired and directly digitalized by scanning electron microscopy has been processed in order to achieve the best conditions to obtain the boundary profile of each particle. The boundary is thus assumed to be representative of the morphological characteristics of the ceramic products. Using the neural approach, a classification accuracy as high as 100% on a training set of 80 sub-images was achieved. These networks correctly classified up to 96.9% of 64 testing patterns not contained in the training set.

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