Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. / Sharifzadeh, Sara; Martinez Vega, Mabel Virginia; Clemmensen, Line H. ; Ersbøll, Bjarne K.

20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013. IEEE, 2013. s. 11-14.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Sharifzadeh, S, Martinez Vega, MV, Clemmensen, LH & Ersbøll, BK 2013, Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. i 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013. IEEE, s. 11-14, International Conference on Systems, Signals and Image Processing 2013, Bucharest, Rumænien, 07/07/2013. https://doi.org/10.1109/IWSSIP.2013.6623437

APA

Sharifzadeh, S., Martinez Vega, M. V., Clemmensen, L. H., & Ersbøll, B. K. (2013). Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. I 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013 (s. 11-14). IEEE. https://doi.org/10.1109/IWSSIP.2013.6623437

Vancouver

Sharifzadeh S, Martinez Vega MV, Clemmensen LH, Ersbøll BK. Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. I 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013. IEEE. 2013. s. 11-14 https://doi.org/10.1109/IWSSIP.2013.6623437

Author

Sharifzadeh, Sara ; Martinez Vega, Mabel Virginia ; Clemmensen, Line H. ; Ersbøll, Bjarne K. / Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013. IEEE, 2013. s. 11-14

Bibtex

@inproceedings{59df654622ff4f7fbb1c905028a57032,
title = "Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data",
abstract = "Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods; lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined",
keywords = "Apples, VIS/NIR, sparse regression, spectroscopy, lasso, elastic-net",
author = "Sara Sharifzadeh and {Martinez Vega}, {Mabel Virginia} and Clemmensen, {Line H.} and Ersb{\o}ll, {Bjarne K.}",
year = "2013",
doi = "10.1109/IWSSIP.2013.6623437",
language = "English",
isbn = "978-1-4799-0941-4",
pages = "11--14",
booktitle = "20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013",
publisher = "IEEE",
note = "null ; Conference date: 07-07-2013 Through 09-07-2013",

}

RIS

TY - GEN

T1 - Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data

AU - Sharifzadeh, Sara

AU - Martinez Vega, Mabel Virginia

AU - Clemmensen, Line H.

AU - Ersbøll, Bjarne K.

N1 - Conference code: 20

PY - 2013

Y1 - 2013

N2 - Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods; lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined

AB - Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods; lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined

KW - Apples

KW - VIS/NIR

KW - sparse regression

KW - spectroscopy

KW - lasso

KW - elastic-net

U2 - 10.1109/IWSSIP.2013.6623437

DO - 10.1109/IWSSIP.2013.6623437

M3 - Article in proceedings

SN - 978-1-4799-0941-4

SP - 11

EP - 14

BT - 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013

PB - IEEE

Y2 - 7 July 2013 through 9 July 2013

ER -

ID: 146201840