Existing remote sensing (RS) change detection (CD) methods typically extract multi-scale features from bi-temporal images, followed by feature fusion and decoding. However, they often overlook spatiotemporal continuity and external correlations, leading to insufficient ability to mine spatiotemporal context. In this paper, we propose a Continuity Information Mining and Integrating Network (CIMINet), which captures and integrates spatiotemporal continuity to reduce pseudo-changes caused by scale differences and limited temporal cues. Spatially, bi-temporal images are processed by Adjacent-Scale Feature Fusion Module (ASFFM) and Spatial Change Extraction Module (SCEM) to extract continuous spatial features and spatial differences. Temporally, pseudo-video frames generated via interpolation help construct temporal continuity, which is further exploited by Temporal Change Extraction Module (TCEM). Finally, we jointly fuse spatial and temporal differences by Spatiotemporal Fusion Module (STFM), highlighting the change regions. Various experimental results indicate that CIMINet outperforms 12 SOTA CD methods on four RS datasets. The code can be found at https://github.com/Aosion518/CIMINet.

Bokeh refocuses the desired regions and generates out-of-focus blur in the remaining regions, which has been well studied for single-image. However, when processing multi-view images of a given scene, the existing Bokeh methods might focus on different objects due to inconsistencies in salient object detection across different views. Additionally, the salient objects with larger depth might be blurred because they are not on the in-focus plane. In this letter, we propose a framework to generate consistent Bokeh across multi-view images. We utilize 3D Gaussian Splatting to render a group of images with a small viewpoint span. To guarantee that the salient objects are identical, we propose aConsistent Saliency Map Generation(CSMG) method with mask tracking. We also propose aDepth Value Reassignment(DVR) method to endow the salient objects with new depth values. With the consistent salient objects and reassigned depth values, we adopt Dr.Bokeh to generate consistent Bokeh effects across multi-view images. Various experiments conducted on the DoF-NeRF dataset demonstrate that our proposed framework outperforms four state-of-the-art bokeh methods on four no-reference image quality evaluation metrics. Code can be found athttps://github.com/jiutaojushi/CBMI.

In this work, we aim to detect the changes caused by object variations in a scene represented by the neural radiance fields (NeRFs). Given an arbitrary view and two sets of scene images captured at different timestamps, we can predict scene changes in that view, which has significant potential applications in scene monitoring and measuring. We conducted preliminary studies and found that such an exciting task cannot be easily achieved by utilizing existing NeRFs and 2D change detection (CD) methods with many false or missing detections. The main reason is that the 2D CD is based on the pixel appearance difference between spatial-aligned image pairs and neglects the stereo information in the NeRF. To address the limitations, we propose the C-NeRF to represent scene changes as directional consistency difference-based NeRF, which mainly contains three modules. We first build two aligned NeRFs from pre-change and post-change scenes. Then, we identify the change points based on the direction-consistent constraint; that is, real change points have similar change representations across view directions, but fake change points do not. Finally, we design the change map rendering process based on the built NeRFs and can generate the change map of an arbitrarily specified view direction. To validate the effectiveness, we build a new dataset containing ten scenes covering diverse scenarios with different changing objects. Our approach surpasses state-of-the-art CD methods and NeRF-based methods by a significant margin.

This study investigates the cognitive impact of dynamic icons in mobile interfaces by integrating electroencephalography (EEG) and eye-tracking technologies. Traditional research on mobile app interface design has relied mainly on questionnaire and eye-tracking methods for behavioral analysis. This research adds a new dimension by examining the neural mechanisms associated with dynamic icons. We employed EEG to analyze channel-wise power spectrum density (PSD), focusing on the alpha and theta frequency bands related to attention and working memory. Concurrently, eye-tracking data were analyzed through Areas of Interest (AOIs) and fixation metrics to assess visual attention patterns. The results indicate that dynamic icons significantly enhance neural activity, with a 15% increase in alpha band power and a 20% increase in theta band power compared to static icons. Additionally, eye-tracking data show a 30% increase in total fixation duration on AOIs containing dynamic icons, particularly in the left-top quarter of the mobile interface. This effect was observed without changes in the first fixation duration, suggesting that dynamic icons have a stronger impact on sustained attention rather than on initial capture. These findings highlight that dynamic icons not only attract and maintain visual attention more effectively but also enhance cognitive processing efficiency. This study provides valuable insights for optimizing mobile app interface design, emphasizing the benefits of incorporating dynamic elements to improve user engagement and interface effectiveness.

Medical image segmentation is significant for diagnosis and prognosis. Inspired by the success of Transformer for Natural Language Processing, this paper presents DBT-UNETR, a novel double branch Transformer architecture with cross fusion for multi-scale feature representation, which enhances the model performance of 3D medical image segmentation. The DBT-UNETR consists of a single scale Transformer and a multiple scale Transformer to capture informative feature. These two branch features are cross fused and then they are combined together. Finally, these feature representations are sent to decoders via skip connection for final segmentation. Moreover, in order to further improve the model performance, an Early Fusion module is designed. Comprehensive experiments are conducted on public available dataset, Multi-Atlas Labeling Beyond The Cranial Vault (BTCV), and it demonstrates the proposed model outperforms the comparative baseline methods.