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Multi-view Geometry for Camera Pose Estiamtion in Visual Odometry and Visual SLAM:
Visual Odometry (VO) and Simultaneous Localization and Mapping (SLAM) are fundamental tasks in computer vision and robotics, enabling autonomous systems to estimate motion from image sequences. VO and Visual SLAM are vision-based technologies used to estimate the relative pose of a camera or robot obtained from a continuous image sequence, that is, changes in the position and orientation of the camera or robot in three-dimensional space. The main task of VO is to estimate the relative pose of the camera from the current frame to the next frame by analyzing the image sequence, which usually includes translation and rotation. This presentation focuses on a novel VO approach for camera pose estimation.
Multi-media Forgery Detection and Localization via Higher-order Network:
With the rapid development of image editing and deepfake synthesis technologies, it has become increasingly easy to modify image, video, or audio using editing or generation techniques, allowing for the creation of false information. The misuse of these technologies poses a severe security challenge to society. Existing methods typically rely on manipulation traces, including artifacts left behind by editing processes and compression. However, these traces are often subtle and easily lost in detail. Therefore, this research proposal uses high-order network modeling to represent complex group relationships. We design multiple hypergraph neural network methods for multi-media forgery detection and localization.
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