Name
Abstract | ||
|---|---|---|
Multispectral imaging is increasingly being used for quality assessment of food items due to its non-invasive benefits. In this paper, we investigate the use of multispectral images of pre-fried carrots, to detect changes over a period of 14 days. The idea is to distinguish changes in quality from spectral images of visible and NIR bands. High dimensional feature vectors were formed from all possible ratios of spectral bands in 9 different percentiles per piece of carrot. We propose to use a multiple hypothesis testing technique based on the Benjamini-Hachberg (BH) method to distinguish possible significant changes in features during the inspection days. Discrimination by the SVM classifier supported these results. Additionally, 2-sided t-tests on the predictions of the elastic-net regressions were carried out to compare our results with previous studies on fried carrots. The experimental results showed that the most significant changes occured in day 2 and day 14. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38886-6_58 | IMAGE ANALYSIS, SCIA 2013: 18TH SCANDINAVIAN CONFERENCE |
Keywords | Field | DocType |
Multispectral imaging, Multiple hypothesis testing, Segmentation, Food quality assessment, SVM classification, Elastic-net regression | Computer vision,Feature vector,Pattern recognition,Computer science,Segmentation,Multispectral image,Multiple comparisons problem,Artificial intelligence,Svm classifier,Spectral bands | Conference |
Volume | ISSN | Citations |
7944 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Sharifzadeh | 1 | 18 | 2.98 |
| Line Harder Clemmensen | 2 | 34 | 6.52 |
| Hanne Løje | 3 | 1 | 0.39 |
| Bjarne Ersbøll | 4 | 450 | 38.06 |