|
Title |
Fiber Classification and Detection Technique Proposed for Applying on the PVA-ECC Sectional Image
|
|
Keywords |
PVA 섬유 ; 인공신경회로망(ANN) ; 분수령 알고리즘 ; 형태학적 재구성 PVA fiber ; categorization ; artificial neural network(ANN) ; watershed algorithm ; morphological reconstruction |
|
Abstract |
The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.
|