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Mental illnesses such as depression are typically neurologically related psychological disorders that affect people’s mood, thinking and behavior. As the number of students who are concerned about their mental health continues to rise, depression has emerged as a mental health concern that has a significant impact on both students’ academic performance and overall lives. To identify depression in students at an earlier stage, the purpose of this study was to provide a potentially unique approach. An approach to the detection of mental illnesses that is based on deep learning networks is proposed in this paper. First, facial expression and physical activity data are utilized for detecting depression. Second, the transformer model is utilized to extract the characteristics of the individual’s physical behavior, and the multiregional attention network (MRAN) is utilized to extract the characteristics of the individual’s emotions. The information that is obtained from the two modalities is complementary. Finally, at the fusion stage, this work applies the classification prediction of depression and nondepression (normal) at the decision level. This is done to ensure that the respective modal properties that were learned by the two channels are preserved in their entirety. We have demonstrated that our strategy is highly effective by performing experimental validation using a dataset that we developed ourselves. It is possible to identify depression in children at an earlier stage with the help of this effective remedy. It is anticipated that the findings of this study will provide an efficient screening tool for depression to educational institutions and organizations that focus on mental health, hence assisting students in receiving essential assistance and intervention at an earlier stage. [ABSTRACT FROM AUTHOR]

The rapid development of augmented reality (AR), 3D reconstruction, simultaneous localization and mapping (SLAM), and autonomous driving requires off-the-shelf camera calibration solutions that are adaptable to cameras of different configurations in different complex scenarios. To this end, we propose a generic, robust, and accurate camera calibration framework, called Meta-Calib, by using single or multiple novel designed ArUco-encoded meta-board(s), which is dedicated to estimate accurate camera intrinsic parameters and extrinsic transformations of different multi-camera configurations. The ArUco calibration board has been redesigned to facilitate learning-based robust detection and obtain higher precision control point coordinates, which is termed the meta-board. This completely replaces the widely-used chessboard based on the corner extraction scheme to greatly alleviate the impact of image distortion on control points, especially when it is located at the boundary area of the fish-eye camera. A robust two-stage deep learning detection strategy is applied to reliably localize the ArUco-encoded inner coding region of the meta-board followed by identifying two categories of circular shapes representing "0" and "1" encoded in the ArUco pattern for decoding and orientation determination. The center points of circular shapes on the meta-board in the distorted image taken under the perspective view can be approximated through elliptical fitting with contour edges. The deviation between the fitting center points and ground-truth can be greatly suppressed when the refined sub-pixel contour edges extracted on the original image are projected to the orthographic projection view based on the camera intrinsic parameters, distortion coefficients and the prior information of the meta-board. Based on this observation, we propose a systematic iterative refinement approach to achieve the high-precision intrinsic calibration of a camera. This process involves improving the estimation of camera intrinsic parameters and fitting the center control points of circular shapes on the meta-boards in an iterative manner. The progressive nature of our approach permits reliably calibrate large distortion camera models under the presence of noisy measurements, which ensures good convergence. In addition, we also propose a graph-based multi-camera extrinsic calibration method via the corrected control points to reliably estimate both the relative poses of the meta-boards and cameras in the multi-camera system. The proposed method is not constrained by the number of cameras and meta-boards used, which makes our strategy accessible even with inflexible computer vision experts. Furthermore, we have derived the mathematical form for computing the covariance of the extrinsic transformation, which makes it possible to evaluate the uncertainty of the calibration results. Extensive experiments on a large number of both real and synthetic datasets, including perspective, fish-eye, and multiple overlapping cameras, are performed to prove the effectiveness and robustness of the developed Meta-Calib calibration framework. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion uses multiple images focused on different depths to generate a clear image covering the whole scene. The existing multi-focus image fusion methods do not consider the defocus successive variation in close-range photography and the camera shaking in sequence image shooting, and most methods cannot process multiple images simultaneously. We proposed an end-to-end deep learning network to generate the all-in-focus image from multi-focus and misaligned images to solve these problems. Specifically, taking multiple multi-focus and misaligned source images as input, our Defocus and Similarity Attention Fusion Network (DSAF-Net) first generates the corresponding defocus map through the Defocus-Net, and warps the source images and defocus maps to a unified camera view according to the optical flow estimated by the OpticalFlow-Net. Finally, our model applies a coarse-to-fine correspondence matching scheme to obtain the similarity weight maps, combined with the warped source images and defocus maps to fuse into a clear image. For training and testing our DSAF-Net, a multi-focus and misaligned cultural heritage photography dataset (WHU-MFM) is constructed using Blender Cycles renderer. Experiments results demonstrate that our method outperforms the state-of-the-art methods both qualitatively and quantitatively. DSAF-Net is available at https://github.com/PeimingCHEN/DSAF-Net. And WHU-MFM is available at https://github.com/PeimingCHEN/WHU-MFM-Dataset. • Proposed an end-to-end neural network based on defocus and similarity attention. • It can fuse several multi-focus and misaligned images simultaneously into a clear image. • Designed an attentional aggregation module for reducing pixel collisions and holes. • Built a first-of-its-kind multi-focus and misaligned close-range photography dataset. [ABSTRACT FROM AUTHOR]