DocumentCode :
3571097
Title :
Halftone Feature Based Classification of Commercial White Print Paper Using BP-MLP
Author :
Mandal, Dipankar ; Chatterjee, Arpitam ; Tudu, Bipan
Author_Institution :
Dept. of Appl. Electron. & Instrum. Eng., Future Inst. of Eng. & Manage., Kolkata, India
fYear :
2014
Firstpage :
233
Lastpage :
236
Abstract :
The paper presents a machine vision approach for classification of the plain surface commercially available printing paper. In the presented method the halftone features are extracted from the images of plain surface papers that are difficult to distinguish due to flat white appearance. The extracted features have been used to train the back-propagation multi-layer perception (BP-MLP) classifier of artificial neural network (ANN). The presented method has been tested with different commercially available A4 size printing paper and found to be promising in terms of classification performance. The paper also reveals that half toning which is conventionally used for display of continuous tone images may be potentially useful for classification tasks, particularly for materials with flat surfaces.
Keywords :
backpropagation; computer vision; feature extraction; image classification; multilayer perceptrons; BP-MLP; BP-MLP classifier; artificial neural network; backpropagation multilayer perception; classification performance; commercial white print paper; half toning; halftone feature based classification; halftone feature extraction; machine vision approach; Accuracy; Artificial neural networks; Feature extraction; Machine vision; Printing; Testing; Training; BP-MLP; Machine vision; halftoning; pattern classifier;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Applications of Information Technology (EAIT), 2014 Fourth International Conference of
Type :
conf
DOI :
10.1109/EAIT.2014.12
Filename :
7052051
Link To Document :
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