Research Statement — Qifa Ke

My primary research interests are computer vision and machine learning, with an emphasis on video analysis and its applications. I believe a working computer vision system should exploit three most fundamental constraints existing in a video sequence: the geometric constraint among video frames and the threedimensional (3D) scene, the coherency in apparent image motions due to scene regularities, and the spatialtemporal statistical redundancy in visual appearance. In doing so, my recent research activities have been focused on the following three topics: • Geometric reconstruction: exploiting the geometric constraint among video frames and the 3D scene; • Layered video representation/analysis: exploiting the image motion coherency due to scene regularities; • Subspace learning: exploiting the spatial-temporal statistical redundancy in a video sequence. The convergence of these research activities is my long term research goal—a computer vision system capable of reconstructing and interpreting the 3D world “seen” in the video. Such a vision system has rich applications in computer graphics and visualization, human computer interaction, robotics, navigation, smart cars, video retrieval/compression, and wearable visual assistant for humans.