Hermes Fandiño-Toro

This work presents a comprehensive analysis of the load stepping method in digital photoelasticity, examined through four complementary perspectives: the classical formulation of load stepping, its extension using polarized-camera technology, a computational hybrid strategy, and exploratory deep learning approaches. Motivation arises from the need to quantify stress fields with greater robustness in contexts where experiments produce multiple fringe map displacements, as occurs under increasing load conditions. This approach aims to overcome the limitations of conventional phase-shifting techniques and lookup-table-based strategies while paving the way for new methodologies capable of generating multiple fringe displacements within a single-shot acquisition framework. The methodology combines synthetic simulations, experimental acquisitions with polarized array and color cameras, and a recent high-sensitivity technique for generating reference fringe-order maps. Demodulated stress fields are compared against these references using standard performance metrics such as MSE, PSNR, SSIM, and the maximum reliably recovered fringe order. The results demonstrate that while the classical approach remains theoretically sound, the polarized-camera update simplifies acquisition, the hybrid strategy reduces optical requirements through computational reconstruction, and deep learning shows promising adaptability to complex and irregular fringe patterns.

Digital photoelasticity enables non-contact, full-field stress analysis through optical fringe patterns, yet its practical deployment is often constrained by experimental complexity and the limited availability of open, standardized datasets. The emergence of multi-polarizer array cameras provides polarization-resolved measurements with high information content, enabling advanced analysis strategies beyond conventional single-image approaches. This work presents a public experimental dataset composed of synchronized image sequences acquired using a polarizer array camera and a conventional RGB camera under incremental mechanical loading. The dataset comprises nine experiments, including four benchmark specimens and five bio-inspired geometries, each recorded over 720 load steps. In total, the dataset releases 25,920 polarization-resolved images and 6480 RGB images, all provided in lossless format and accompanied by experiment-specific segmentation templates. Although classical and hybrid load-stepping methods are used to demonstrate the utility of the dataset, its scope is not limited to this application. The dataset is intended as a flexible platform for exploring a wide range of photoelastic analysis techniques that leverage polarization information, while enabling direct comparison with conventional color demodulation techniques.

As the world's population ages, the prevalence of rheumatic diseases is increasing. These diseases are often diagnosed and monitored using imaging techniques such as infrared thermography. In this study, we developed an application to calculate the thermographic index (TI) in a series of thermograms. Such index is used in rheumatology as a complementary diagnostic tool to assess pain and swelling. We designed the application in MATLAB App Designer; an environment for creating applications with a graphical user interface. To evaluate the performance of the developed application, we calculated the TI of ten trigger points (anterior shoulder, sternum, posterior shoulder, upper back, lower back, elbow, lateral shoulder, elbow pit, knee, and back of the knee) in a series of thermal images from 37 healthy participants. Results of the average temperature varied between 31.35 (knee for women) and 33.92 (lateral shoulder for men) in °C and TIs were lower than 2 and the results in the knee region are consistent with those reported in previous studies due to only the TI in the knee is known. This study contributes to the literature with reference values for nine trigger points other than the knee. This app is easy to use and may be useful in future applications for disease detection or monitoring of its progression.

When experimental photoelasticity images are acquired, the spectral interaction between the light source and the sensor used affect the visual information of the fringe patterns in the produced images. Such interaction can lead to fringe patterns with an overall high quality, but also can lead to images with indistinguishable fringes, and bad stress field reconstruction. We introduce a strategy to evaluate such interaction that relies on measuring the value of four handcrafted descriptors: contrast, an image descriptor that accounts simultaneously for blur and noise, a Fourier-based descriptor to measure image quality, and image entropy. The utility of the proposed strategy was validated by measuring the selected descriptors on computational photoelasticity images, and the fringe orders achieved when evaluating the stress field, from 240 spectral configurations: 24 light sources and 10 sensors. We found that high values of the selected descriptors can be related to spectral configurations that lead to better stress field reconstruction. Overall, the results show that the selected descriptors can be useful to identify bad and good spectral interactions, which could help to design better protocols for acquiring photoelasticity images.

The interactions between light sources and sensors alter the spatial frequency content of fringe patterns in photoelasticity studies. This paper presents a computational strategy to evaluate these alterations in the frequency domain.

Bayer filters and demosaicking methods applied to photoelasticity cause errors when imaging the stress field. This paper presents a texture analysis to quantify these errors.

In digital photoelasticity, fringe pattern analysis is crucial because the photoelastic fringes provide information about direction and magnitudes of the principal stresses at the surface of the inspected object. These fringes exhibit visual properties that depend on the applied load, their spatial location in the inspected object geometry, and the illumination source. Traditional methods for fringe analysis in photoelasticity have limited performance when dealing with noisy or not well contrasted fringes, or if the spatial resolution of the fringes is lost. This work presents an approach for analyzing fringe patterns in photoelasticity images using texture information, in conjunction with machine learning techniques. Stress fields are simulated in multiple spectral bands for two models. Then, different regions of interest in these models are characterized with well-known texture descriptors. Furthermore, feature ranking and five classification schemes are used to describe the texture variations that occur in the models when they undergo diametral compression in the different spectral bands considered. The results show that texture descriptors are suitable tools for describing the stress information provided by photoelastic fringe patterns. Also, it is possible to use machine learning techniques to learn, recognize, and predict the behavior of models subjected to mechanical load in photoelasticity experiments.

In digital photoelasticity, evaluating the stress map is often affected in regions with critical values. This phenomenon is associated to color degradation effect and high fringe densities. It is a consequence of different experimental conditions, such as: type of birefringent material, relative spectral content of light source, relative spectral response of camera sensor, polarization optical elements, load application, etc. In this study field, the main goal accounts for evaluating the stress values, as better as possible, from photoelasticity images. Which turns the view towards the process that allow to acquire photoelasticity images with more complete information. This makes necessary to analyze the possible effects that each element could introduce into the photoelasticity image generation. This paper presents a computational analysis on the effect that different industrial light sources introduces for recovering the stress maps. Hence, four common industrial light sources are considered for generating the photoelasticity images. In this case, results reveal that there are light sources which represent stronger limitations for evaluating the stress, and that Such effect varies with the load increments. This approach is useful for predicting the possible effect that a light source selection could introduce into the stress evaluation process.