| dc.contributor.author |
Shahin, MA |
en |
| dc.contributor.author |
Tollner, EW |
en |
| dc.contributor.author |
Evans, MD |
en |
| dc.contributor.author |
Arabnia, HR |
en |
| dc.date.accessioned |
2014-06-06T06:43:49Z |
|
| dc.date.available |
2014-06-06T06:43:49Z |
|
| dc.date.issued |
1999 |
en |
| dc.identifier.issn |
00012351 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/1479 |
|
| dc.relation.uri |
http://www.scopus.com/inward/record.url?eid=2-s2.0-0033232565&partnerID=40&md5=e5940e33ebf38a88f32258c499875256 |
en |
| dc.subject |
Apple |
en |
| dc.subject |
Bayesian classifier |
en |
| dc.subject |
Cosine transform |
en |
| dc.subject |
DCT |
en |
| dc.subject |
DWT |
en |
| dc.subject |
Feature selection |
en |
| dc.subject |
Image |
en |
| dc.subject |
Image features |
en |
| dc.subject |
Watercore |
en |
| dc.subject |
Wavelets |
en |
| dc.subject |
X-rays |
en |
| dc.subject.other |
Cosine transforms |
en |
| dc.subject.other |
Feature extraction |
en |
| dc.subject.other |
Nondestructive examination |
en |
| dc.subject.other |
Statistical methods |
en |
| dc.subject.other |
X rays |
en |
| dc.subject.other |
Bayesian classifier |
en |
| dc.subject.other |
Feature selection |
en |
| dc.subject.other |
Watercore |
en |
| dc.subject.other |
Fruits |
en |
| dc.subject.other |
Malus x domestica |
en |
| dc.title |
Watercore features for sorting red delicious apples: A statistical approach |
en |
| heal.type |
journalArticle |
en |
| heal.publicationDate |
1999 |
en |
| heal.abstract |
Watercore is a serious internal defect found in most apple cultivars. X-ray imaging has shown potential for nondestructive detection of watercore, however, the major challenge is to decide which features should be used for fruit classification. A solution to this problem is feature selection based on their rank ordering. This article describes a stepwise feature selection procedure. The performance of the selected features was tested using the Bayes classifier. Spatial features (area, intensity) and (cosine, wavelet) transform coefficients were evaluated for their discriminating power. The spatial features performed better than the transform features. A linear Bayesian classifier with three input features (fruit area in the segmented image, mean intensity of fruit in the original image, and 10th harmonic of the discrete cosine transform) achieved an accuracy of 79%.Watecore is a serious internal defect found in most apple cultivars. X-ray imaging has shown potential for nondestructive detection of watercore, however, the major challenge is to decide which features should be used for fruit classification. A solution to this problem is feature selection based on their rank ordering. This article describes a stepwise feature selection procedure. The performance of the selected features was tested using the Bayes classifier. Spatial features (area, intensity) and (cosine, wavelet) transform coefficients were evaluated for their discriminating power. The spatial features performed better than the transform features. A linear Bayesian classifier with three input features (fruit area in the segmented image, mean intensity of fruit in the original image, and 10th harmonic of the discrete cosine transform) achieved an accuracy of 79%. |
en |
| heal.publisher |
ASAE, St. Joseph, MI, United States |
en |
| heal.journalName |
Transactions of the American Society of Agricultural Engineers |
en |
| dc.identifier.issue |
6 |
en |
| dc.identifier.volume |
42 |
en |
| dc.identifier.spage |
1889 |
en |
| dc.identifier.epage |
1896 |
en |