Machine vision scheme for stain-release evaluation using Gabor filters with optimized coefficients |
| |
Authors: | Cui Mao Arunkumar Gururajan Hamed Sari-Sarraf Eric Hequet |
| |
Affiliation: | (1) Faculty of Engineering, Multimedia University, Jalan Multimedia, Cyberjaya, Selangor, 63100, Malaysia;(2) School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK |
| |
Abstract: | 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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|