Melamine Faced Panels Defect Classification beyond the Visible Spectrum

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution

Robotics Education in STEM Units: Breaking Down Barriers in Rural Multigrade Schools

We report a novel proposal for reducing the digital divide in rural multigrade schools, incorporating knowledge of robotics with a STEM approach to simultaneously promote curricular learning in mathematics and science in several school grades. We used an exploratory qualitative methodology to implement the proposal with 12 multigrade rural students. We explored the contribution of the approaches to the promotion of curricular learning in mathematics and science and the perceptions of using robotics to learn mathematics and science. As data collection techniques, we conducted focus groups and semi-structured interviews with the participants and analyzed their responses thematically. We concluded that the proposal could contribute to meeting the challenges of multigrade teaching. Our findings suggest that the proposal would simultaneously promote the development of curricular learning in mathematics and science in several school grades, offering an alternative for addressing various topics with different degrees of depth

Feature Point Descriptors: Infrared and Visible Spectra

This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR)

Evaluation of the accuracy of T-SPOT.TB for the diagnosis of ocular tuberculosis in a BCG-vaccinated, non-endemic population

Purpose: To determine the performance of T-SPOT.TB, an interferon gamma release assay test, in patients with ocular tuberculosis (TB) in a BCG-vaccinated, non-endemic population. Methods: We employed a nested case control design. In total, 45 subjects were enrolled (23 patients with ocular tuberculosis and 22 patients with other causes of uveitis). A blood sample was collected from each subject, and T-SPOT.TB was executed. Laboratory professionals were blinded to the disease status of each subject. Results: Five patients were excluded because of indeterminate results. The calculated sensitivity and specificity were 0.80 and 0.85, respectively. The positive likelihood ratio was 5.33 and the negative likelihood ratio was 0.23. The overall accuracy of the test was 0.83. Conclusions: T-SPOT.TB adequately diagnosed ocular TB. This technique is particularly useful in populations where BCG vaccinations are still mandatory