2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images

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doi:10.1093/ietfec/e89-a.5.1484

2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images

Ruen MEYLAN, Cenker ODEN, Ayn ERTUZUN, Aytul ERÇL

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In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.5 pp.1484-1494

Publication Date: 2006/05/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e89-a.5.1484

Type of Manuscript: PAPER

Category: Image

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Ruen MEYLAN, Cenker ODEN, Ayn ERTUZUN, Aytul ERÇL, "2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images" in IEICE TRANSACTIONS on Fundamentals, vol. E89-A, no. 5, pp. 1484-1494, May 2006, doi: 10.1093/ietfec/e89-a.5.1484.
Abstract: In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.5.1484/_p

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@ARTICLE{e89-a_5_1484,
author={Ruen MEYLAN, Cenker ODEN, Ayn ERTUZUN, Aytul ERÇL, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images},
year={2006},
volume={E89-A},
number={5},
pages={1484-1494},
abstract={In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.},
keywords={},
doi={10.1093/ietfec/e89-a.5.1484},
ISSN={1745-1337},
month={May},}

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TY - JOUR
TI - 2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1484
EP - 1494
AU - Ruen MEYLAN
AU - Cenker ODEN
AU - Ayn ERTUZUN
AU - Aytul ERÇL
PY - 2006
DO - 10.1093/ietfec/e89-a.5.1484
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2006
AB - In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.
ER -