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.

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.

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.

Brain Computer Interface (BCI) covers an area of special interest, mainly due to the research being conducted to control external devices via thought commands, generating solutions in both motor disability and speech. These applications require the use of signal processing in real time, to be used in devices that help people with this type of disabilities. This paper presents a methodology for feature extraction of electroencephalographic (EEG) signals in motor imagery task of both left and right hand, using the public database BCI Competition 2003. It has been used the wavelet transform for the signal decomposition in the spectral bands of interest (known as brain rhythms). The brain rhythms characterization was conducted through relative energy, variance and standard deviation of the wavelet coefficients. In addition, we conducted the relevance analysis through the fuzzy entropy algorithm, to find the most important features within the training set. We obtained a classification accuracy of up to 98.44% using K-NN and SVM algorithms. The classification results allow inferring that the methodology is appropriate for the recognition of imagination movements in people with motor disabilities and could generate solutions in applications of BCI systems.

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 the image. 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.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.