In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas-Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss-Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.

This research investigates the challenges and opportunities rural elementary teachers perceive in using AI as a pedagogical tool to support student learning in rural schools. Using a convergent parallel mixed methods approach, we analyzed the responses from 45 rural teachers who participated in professional development on AI integration in rural education. Through both closed-ended and open-ended survey responses, we employed an adaptation of the TPACK framework (I-TPACK) and the AI literacy framework proposed by UNESCO to identify the primary challenges and opportunities in utilizing AI for pedagogical purposes in rural education. The results highlight resource accessibility and teacher professional development as critical challenges and opportunities to reduce the digital divide in rural communities. Teachers see the inclusion of AI as an opportunity to personalize learning, reduce workload, and facilitate teaching in multigrade classrooms without perceiving it as a job threat. At the same time, they emphasize the need for technological and didactic resources aligned with the specific characteristics of their contexts, such as offline resources and adaptable AI curricula to address the prevalent issue of limited or absent internet connectivity in many rural schools.

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.

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).

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.

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.