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Quality inspection for cheese packag...

Cheng, Zhe.

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Quality inspection for cheese packaging using machine vision and image processing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Quality inspection for cheese packaging using machine vision and image processing./
Author:	Cheng, Zhe.
Description:	102 p.
Notes:	Source: Masters Abstracts International, Volume: 52-03.
Contained By:	Masters Abstracts International52-03(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1548021
ISBN:	9781303523045

Quality inspection for cheese packaging using machine vision and image processing.
Cheng, Zhe.

Quality inspection for cheese packaging using machine vision and image processing. - 102 p.

Source: Masters Abstracts International, Volume: 52-03.

Thesis (M.S.E.)--Purdue University, 2013.

The acumen and sophistication of consumers have created the increasing expectation for improved quality in food product, which is considered as the essential element of daily life. In turn, this has encouraged food producers to improve their quality monitoring by deploying enhanced computer quality inspection technologies [1]. The aim of this thesis is to design an efficient and adaptable algorithm to accurately and efficiently monitor the quality of the packaged cheeses on the assembly line. Computer vision and image processing methods were used to distinguish unqualified cheeses from a large amount of samples. The criteria for classification were consisted of two main aspects, similarity of cheese shape and leakage condition. Gray and binary cheese images were converted from the original pictures, which were captured by cameras. The cheese part was extracted from the background for shape analysis to generate its signature, which was then compared with the signature of the standard cheese shape to measure the similarity by the cross-correlation method. Cheese leakage in the remaining part was discovered by setting a certain range of RGB value, which was subject to the condition of light sources. Two thresholds were set to control the detection result, which was intended x to best match human perception. Sensitivity, specificity, accuracy and receiver operating characteristic (ROC) were used to evaluate the algorithm's performance.

ISBN: 9781303523045Subjects--Topical Terms:

1669061
Engineering, Computer.

Quality inspection for cheese packaging using machine vision and image processing.
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035 $a (MiAaPQ)AAI1548021
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100 1 $a Cheng, Zhe. $3 1270890
245 1 0 $a Quality inspection for cheese packaging using machine vision and image processing.
300 $a 102 p.
500 $a Source: Masters Abstracts International, Volume: 52-03.
500 $a Adviser: Bin Chen.
502 $a Thesis (M.S.E.)--Purdue University, 2013.
520 $a The acumen and sophistication of consumers have created the increasing expectation for improved quality in food product, which is considered as the essential element of daily life. In turn, this has encouraged food producers to improve their quality monitoring by deploying enhanced computer quality inspection technologies [1]. The aim of this thesis is to design an efficient and adaptable algorithm to accurately and efficiently monitor the quality of the packaged cheeses on the assembly line. Computer vision and image processing methods were used to distinguish unqualified cheeses from a large amount of samples. The criteria for classification were consisted of two main aspects, similarity of cheese shape and leakage condition. Gray and binary cheese images were converted from the original pictures, which were captured by cameras. The cheese part was extracted from the background for shape analysis to generate its signature, which was then compared with the signature of the standard cheese shape to measure the similarity by the cross-correlation method. Cheese leakage in the remaining part was discovered by setting a certain range of RGB value, which was subject to the condition of light sources. Two thresholds were set to control the detection result, which was intended x to best match human perception. Sensitivity, specificity, accuracy and receiver operating characteristic (ROC) were used to evaluate the algorithm's performance.
590 $a School code: 0183.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Agriculture, Food Science and Technology. $3 1017813
650 4 $a Engineering, Packaging. $3 1025152
690 $a 0464
690 $a 0359
690 $a 0549
710 2 $a Purdue University. $b Computer Engineering. $3 2100611
773 0 $t Masters Abstracts International $g 52-03(E).
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791 $a M.S.E.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1548021