Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Image Based View Synthesis of Articulated Agents
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Journal of Cognitive Neuroscience
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Tracking the pose of an object is a fundamental operation in computer vision. Yet, achieving this task for arbitrary objects without requiring a priori knowledge remains a major stumbling block. This paper introduces a method for tracking the pose of a moving object without requiring its 3D model or textured surfaces. In the first step, a sequence of images-poses pairs is obtained and PCA coefficients are derived from the image sequence. Then, a piecewise linear observation mapping is build between the poses and the PCA coefficients. The mapping is then used in the observation model of a Kalman filter that tracks the pose of the object.