In digital photoelasticity images, regions with high fringe densities represent a limitation for unwrapping the phase in specific zones of the stress map. In this work, we recognize such regions by varying the light source wavelength from visible to far infrared, in a simulated experiment based on a circular polariscope observing a birefringent disk under diametral compression. The recognition process involves evaluating the relevance of texture descriptors applied to data sets extracted from regions of interest of the synthetic images, in the visible electromagnetic spectrum and different sub-bands of the infrared. Our results show that extending photoelasticity assemblies to the far infrared, the stress fields could be resolved in regions with high fringe concentrations. Moreover, we show that texture descriptors could overcome limitations associated to the identification of high-stress values in regions in which the fringes are concentrated in the visible spectrum, but not in the infrared.
Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection.
It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those
flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest
(ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology
for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform
heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in
In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is
robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian
scale analysis and local edge detection. In this methodology local correlation between image and Gaussian
window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well
modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using
multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size.
Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide
a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the
other dedicate algorithms proposed in the state of art.