TY - JOUR
T1 - Machine vision scheme for stain-release evaluation using Gabor filters with optimized coefficients
AU - Mao, Cui
AU - Gururajan, Arunkumar
AU - Sari-Sarraf, Hamed
AU - Hequet, Eric
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012/3
Y1 - 2012/3
N2 - This paper presents an efficient and practical approach for automatic, unsupervised object detection and segmentation in two-texture images based on the concept of Gabor filter optimization. The entire process occurs within a hierarchical framework and consists of the steps of detection, coarse segmentation, and fine segmentation. In the object detection step, the image is first processed using a Gabor filter bank. Then, the histograms of the filtered responses are analyzed using the scale-space approach to predict the presence/absence of an object in the target image. If the presence of an object is reported, the proposed approach proceeds to the coarse segmentation stage, wherein the best Gabor filter (among the bank of filters) is automatically chosen, and used to segment the image into two distinct regions. Finally, in the fine segmentation step, the coefficients of the best Gabor filter (output from the previous stage) are iteratively refined in order to further fine-tune and improve the segmentation map produced by the coarse segmentation step. In the validation study, the proposed approach is applied as part of a machine vision scheme with the goal of quantifying the stain-release property of fabrics. To that end, the presented hierarchical scheme is used to detect and segment stains on a sizeable set of digitized fabric images, and the performance evaluation of the detection, coarse segmentation, and fine segmentation steps is conducted using appropriate metrics. The promising nature of these results bears testimony to the efficacy of the proposed approach.
AB - This paper presents an efficient and practical approach for automatic, unsupervised object detection and segmentation in two-texture images based on the concept of Gabor filter optimization. The entire process occurs within a hierarchical framework and consists of the steps of detection, coarse segmentation, and fine segmentation. In the object detection step, the image is first processed using a Gabor filter bank. Then, the histograms of the filtered responses are analyzed using the scale-space approach to predict the presence/absence of an object in the target image. If the presence of an object is reported, the proposed approach proceeds to the coarse segmentation stage, wherein the best Gabor filter (among the bank of filters) is automatically chosen, and used to segment the image into two distinct regions. Finally, in the fine segmentation step, the coefficients of the best Gabor filter (output from the previous stage) are iteratively refined in order to further fine-tune and improve the segmentation map produced by the coarse segmentation step. In the validation study, the proposed approach is applied as part of a machine vision scheme with the goal of quantifying the stain-release property of fabrics. To that end, the presented hierarchical scheme is used to detect and segment stains on a sizeable set of digitized fabric images, and the performance evaluation of the detection, coarse segmentation, and fine segmentation steps is conducted using appropriate metrics. The promising nature of these results bears testimony to the efficacy of the proposed approach.
KW - Fabric stain release
KW - Filter-bank
KW - Gabor filter
KW - Gabor filter optimization
KW - Histogram analysis
KW - Object detection
KW - Optimal thresholding
KW - Scale space
KW - Texture segmentation
UR - http://www.scopus.com/inward/record.url?scp=84861617210&partnerID=8YFLogxK
U2 - 10.1007/s00138-010-0295-7
DO - 10.1007/s00138-010-0295-7
M3 - Review article
AN - SCOPUS:84861617210
VL - 23
SP - 349
EP - 361
JO - Machine Vision and Applications
JF - Machine Vision and Applications
SN - 0932-8092
IS - 2
ER -