The Factorization Method for Image Sequence Analysis

今年是连续三年获奖的第一年。 的 这项研究的长期目标是准确的恢复 摄像机运动和密集的三维形状信息， 视频速率的电视图像。 在以前的工作中，校长 研究者开发了一种基于矩阵的因子分解方法， 该任务基于从帧到帧跟踪的一组特征点， 密集图像序列的帧。 方法的实验 与现有技术相比， 形状和运动恢复系统。 这项研究现在将 开发图像噪声敏感性的表征 序列分析，探索高效增量数值 方法进行因式分解，并重新制定方法， 透视投影、多个运动和密集形状 结果 作为一个新的研究领域的探索性进展 运动分析和多媒体之间的联系的调查 还将进行目标识别的框架。 这 研究应有助于更好地理解理论 视觉运动分析，并产生一个视觉模块， 让机器人在环境中定位自己，绘制一张地图， 自己的导航和避障环境，以及 感知物体的形状以便识别或操纵 他们

This is the first year of a three-year continuing award. The long-term goal of this research is the recovery of accurate camera motion and dense three-dimensional shape information from television images at video rate. In prior work, the Principal Investigator developed a matrix-based factorization method for this task, based on a set of feature points tracked from frame to frame of a dense image sequence. Experiments with the method demonstrated a dramatic performance improvement over existing shape and motion recovery systems. This research will now develop a characterization of the noise sensitivity of image sequence analysis, explore efficient and incremental numerical methods for factorization, and reformulate the method for perspective projection, multiple motions, and dense shape results. As an exploratory advance into a new area of research an investigation of the link between motion analysis and multi- frame of object recognition will also be conducted. This research should contribute to a better theoretical understanding of visual motion analysis, and produce a vision module that would let a robot localize itself in the environment, draw a map of its own surroundings for navigation and obstacle avoidance, and perceive the shape of objects in order to recognize or manipulate them.