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TEST PRINTED IMAGE DIRECTLY--The method of locating printed image and recognizing dots of being tested images

Author: WangShiYun
Tutor: LiuShiChang
School: Xi'an University of Technology
Course: Pulp and Paper Engineering
Keywords: printed image testing directly image locating decision function of distance neural network
CLC: TS801
Type: Master's thesis
Year: 2002
Downloads: 404
Quote: 9
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Abstract


To overcome the defect of control strip in printing quality testing, the idea about printed image testing directly has been suggested in this article. The being tested images have been located strictly by the means of digital image processing to make the location accuracy better to by mechanical means. Because Neugebauer equation can’ t compute dot coverage area in general image testing directly, the dots in primary color have been segmented by pattern recognition, and the area of every colored dots have been computed secondly in this article.In the chapter of locating, the standard image and the being tested images have been rotated firstly to remove the angle between the image coordinate and the right-angled coordinate in a plane, a sub-image A with a fixed area has been cut at the fixed location in the being tested image and moved in the standard image each line and each volume to find the most similar sub-image B. The location of sub-image B has been recorded, the most similar two pairs of location has been acquired by the means of Geometric moving androtating.On the base of locating, the different colored dots have been classifiedby pattern recognition in the fixed location and area sub-image to analyzethe parameter of dots and finish image testing directly. The colors of dothave been made to be characterization vector in order to train recognition model in this article, classifying models have been set up by decision function of distance and neural network, the data about dot area coverage from classifying models have been compared with that from Neugebauer equation to prove that different colored dots can be classified by pattern recognition. And the data has been analyzed in theory and practice to prove that the neural network model is better than statistic model to classify the printed dots.

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CLC: > Industrial Technology > Light industry,handicrafts > Printing Industry > General issues > Printing and basic science
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