Name
Abstract | ||
|---|---|---|
The acumen and sophistication of consumers have created the increasing expectation for improved quality in food products. This in turn has boosted the need for enhanced quality monitoring [1]. The aim of this project is to develop an efficient and adaptable algorithm to accurately monitor the quality of packaged cheeses. Computer vision was used to distinguish unqualified cheeses from a large number of samples. The criteria for classification consisted of two main aspects, similarity of cheese shape and leakage condition. Gray and binary cheese images were obtained from the original pictures, which were captured by Cognex® cameras. The cheese part was extracted from the background for shape analysis. A signature of the extracted cheese region was compared with the signature of the standard cheese shape for classification. The similarity between the cheese and template was measured 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 to best match human perception. Sensitivity, specificity, accuracy and receiver operating characteristic (ROC) were used to evaluate the algorithm's performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EIT.2012.6220766 | EIT |
Keywords | Field | DocType |
rgb value,packaging,human perception,shape recognition,production engineering computing,quality monitoring,cheese shape similarity,food product quality,classification criteria,gray cheese image,receiver operating characteristic,computerised monitoring,binary cheese image,cheese region extraction,machine vision,automatic optical inspection,roc,feature extraction,image classification,cameras,dairy products,computer vision,light source,cheese packaging,cross-correlation method,cognex camera,quality inspection improvement,cheese shape analysis,cheese leakage,correlation methods,product quality,image colour analysis,shape,sensitivity,cross correlation,classification algorithms,inspection,receiver operator characteristic,shape analysis | Computer vision,Receiver operating characteristic,Machine vision,Computer science,Food products,Electronic engineering,Feature extraction,RGB color model,Artificial intelligence,Statistical classification,Contextual image classification,Shape analysis (digital geometry) | Conference |
ISSN | ISBN | Citations |
2154-0357 | 978-1-4673-0819-9 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Wang | 1 | 2 | 0.74 |
| Zhe Cheng | 2 | 0 | 0.34 |
| Junxuan Hou | 3 | 0 | 0.34 |
| Dongjian Cai | 4 | 0 | 0.68 |
| Bin Chen | 5 | 4 | 6.85 |