Most existing emotion recognition studies rely on discrete stimulus paradigms under controlled laboratory conditions, failing to capture the inherently dynamic and transitional nature of emotional states encountered in real-world operational environments. To address this gap, we propose a novel approach for emotion transition recognition using multimodal physiological signal fusion. Thirty participants, including 10 licensed pilots and 20 non-pilots controls, were recruited to view 48 video stimuli de-signed to elicit six distinct emotional transitions. We introduce a two-stage progressive recognition framework to enhance performance in emotional transition modeling. In Stage I, differential entropy features extracted from EEG signals are integrated with Big Five personality traits to construct an occupation-based trait classification model, capturing cognitive-emotional patterns associated with occupational background and providing individualized priors. In Stage II, we develop the temporal spatial embed ding bidirectional cross-attention fusion network (TSE-BCAF), which employs parallel convolutional modules to extract deep spatial features from multimodal EEG and EOG signals. These features are fused via a bidirectional cross-attention mechanism to model inter-modal interactions, then processed by a Trans former-based temporal encoder to capture the evolving dynamics of emotional transitions. Experimental results demonstrate that the proposed framework significantly outperforms unimodal and state-of-the-art multimodal baselines in both accuracy and robustness. The findings highlight the potential of our approach for real-world applications in aviation psychology and other high stakes domains requiring adaptive emotional regulation.

Though the accuracy and robustness of optical flow has been dramatically enhanced over the past few years, the issue of edge-blurring near the image and motion boundaries has remained a challenge in flow field estimation. In this paper, we propose a refined total variation withL1norm (TV-L1) optical flow estimation approach using joint filtering, named JOF. First, we divide the image into three categorized regions: mutual-structure regions, inconsistent structure regions, and smooth regions. The mutual-structure guided filter for optical flow estimation is constructed by extracting the mutual-structure regions of the flow field. Second, the refined TV-L1optical flow model is proposed by incorporating the non-local term and mutual-structure guided filter objective function into the classical TV-L1energy function. Furthermore, the novel TV-L1optical flow objective function is minimized using a joint filtering program composed of a weighted median filter and a mutual-structure guided filter to optimize the estimated flow field during the coarse-to-fine optical flow computation scheme. Finally, we compare the proposed JOF method with several state-of-the-art approaches including variational and deep learning based optical flow models using the Middlebury, MPI-Sintel, and UCF101 test databases. The evaluation results indicate that the proposed method has high accuracy and good robustness for flow field computation and, especially, the significant benefit of edge-preserving.