Functional brain network construction methods are crucial for understanding neurological and psychiatric disorders, yet their stability and consistency across different parameters remain poorly understood. This study systematically evaluates the robustness of two widely used EEG-based functional connectivity methods: imaginary part of coherency combined with cluster span threshold (ICoh+CST) and phase lag index combined with cluster span threshold (PLI+CST). We analyzed EEG data from two independent datasets across multiple epoch lengths (120 seconds) at both scalp and source levels. Network metrics including global efficiency and local efficiency were computed across four frequency bands (delta, theta, alpha, beta). Results demonstrate that the ICoh+CST method achieves stability at significantly shorter epoch lengths compared to PLI+CST (10s vs. 14s for scalp-level analysis, 6s vs. 12s for source-level analysis). Linear regression analysis revealed that ICoh+CST exhibits lower correlation between network metric variability and epoch length changes, indicating superior robustness. Importantly, the ICoh+CST method yielded more consistent findings across datasets, with major depressive disorder patients showing significantly increased global efficiency in the theta band at the source level in both datasets. Our results have important implications for standardizing neuroimaging protocols and improving the reproducibility of functional connectivity studies in clinical neuroscience research.

Three-dimensional reconstruction of dynamic objects is important for robotic applications, for example, the robotic recognition and manipulation. In this paper, we present a novel 3D surface reconstruction method for moving objects. The proposed method combines the spatial-multiplexing and time-multiplexing structured-light techniques that have advantages of less image acquisition time and accurate 3D reconstruction, respectively. A set of spatial-temporal encoded patterns are designed, where a spatial-encoded texture map is embedded into the temporal-encoded three-step phase-shifting fringes. The specifically designed spatial-coded texture assigns high-uniqueness codeword to any window on the image which helps to eliminate the phase ambiguity. In addition, the texture is robust to noise and image blur. Combining this texture with high-frequency phase-shifting fringes, high reconstruction accuracy would be ensured. This method only requires 3 patterns to uniquely encode a surface, which facilitates the fast image acquisition for each reconstruction step. A filtering stereo matching algorithm is proposed for the spatial-temporal multiplexing method to improve the matching reliability. Moreover, the reconstruction precision is further enhanced by a correspondence refinement algorithm. Experiments validate the performance of the proposed method including the high accuracy, the robustness to noise and the ability to reconstruct moving objects.

Autonomous vehicles often rely on high-definition (HD) maps to navigate around. However, lane markings (LMs) are not necessarily static objects due to wear & tear from usage and road reconstruction & maintenance. Therefore, the wrong matching between LMs in the HD map and sensor readings may lead to erroneous localization or even cause traffic accidents. It is imperative to keep LMs up-to-date. However, frequently recollecting data with dedicated hardware and specialists to update HD maps is not only cost-prohibitive but also unviable. Here we propose to utilize crowdsourced images from multiple vehicles at different times to help verify LMs for HD map maintenance. We obtain the LM distribution in the image space by considering the camera pose uncertainty in perspective projection. Both LMs in HD map and LMs in the image are treated as observations of LM distributions which allow us to construct posterior conditional distribution (a.k.a Bayesian belief functions) of LMs from either sources. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. We verify our approach using real data. Experimental results show that our method is capable of verifying and updating LMs in the HD map.

The development of sharing technology makes it possible for expensive lower limb exoskeleton robots to be extensively employed. However, due to the uniqueness of gait pattern, it is challenging for lower limb exoskeleton robot to adapt to different wearers' gait patterns. Studies have shown that the gait pattern is affected by many physical factors. This paper proposes an individualized gait pattern generation (IGPG) method for sharing lower limb exoskeleton (SLEX) robot. First, the gait sequences are parameterized to extract gait features. Then, the Gaussian process regression with automatic relevance determination is used to establish the mapping relationships between the body parameters and the gait features, and the weights of each body parameters on gait pattern are also given. The gait features of an unknown subject can be predicted based on the training set. Finally, the individualized gait pattern is reconstructed by autoencoder neural network and scaling process based on predicted gait features. The experimental results show that the gait pattern predicted by IGPG is very similar to the subject's actual trajectory and has been successfully applied on the SLEX robot. With the help of sharing technology, the training set will be increased, and the prediction accuracy of individualized gait pattern will also be improved.

In this paper, a method is proposed to search for spatio-temporal path for action localization in unconstrained videos. We mainly focus on two requirements, i.e., accurate human extraction and speeding generation of action proposal. The approach first generates human proposals at the frame level, then scores them based on two complementary parts, i.e., posteriori probability evaluated via a fine-tuned Faster-RCNN and template-matching similarity based on the spatiotemporal continuity. Finally, the generation of action proposal is formulated as a Max-Path discovery problem, coupled with dynamic programming to find an optimal path with maximum score. Experiments on UCF-Sports are performed to verify that the proposed method can achieve fast high-quality action proposal and link the missed-detection proposals in successive frames together to form a complete action.

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

In this paper, we present a novel approach for video anomaly detection in crowded scenes. The proposed approach detects anomalies based on the contextual information analysis within spatio-temporal video volume. Around each pixel, spatio-temporal volumes are built and clustered to construct the activity pattern codebook. Then, the composition information of the volumes within a large spatiotemporal window is described via a dictionary learned by sparse representation. Furthermore, multi-scale analysis is employed to adapt the size change of abnormal events. Finally, the sparse reconstruction cost is designed to evaluate the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the existing public available anomaly-detection datasets and the performance comparasion with three existing methods validates that the proposed method detects anomalies more accurately.