Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: Inferring scene geometry and camera motion from a stream of 
        images is possible in principle, but is an ill-conditioned 
        problem when the objects are distant with respect to their size.
        We have developed a factorization method that can overcome this 
        difficulty by recovering shape and motion without computing 
        depth as an intermediate step.
        
        An image stream can be represented by the 2F*P measurement 
        matrix of the image coordinates of P points tracked through F 
        frames. 
        We show that under orthographic projection this matrix is of 
        rank 3.
        Using this observation, the factorization method uses the 
        singular value decomposition technique to factor the measurement
        matrix into two matrices which represent object shape and camera
        motion respecrtively.
        The method can also handle and obtain a full solution from a 
        partially filled-in measurement matrix, which occurs when 
        features appear and disappear in the image sequence due to 
        occlusions or tracking failures.
        
        The method gives accurate results, and does not introduce 
        smoothing in either shape or motion. 
        We demonstrate this with a series of experiments on laboratory 
        and outdoor image streams, with and without occlusions.
Number: CMU-CS-92-104
Bibtype: TechReport
Month: jan
Author: Carlo Tomasi 
        Takeo Kanade
Title: Shape and Motion from Image Streams:
        a Factorization Method --- Parts 2,8,10
Year: 1992
Address: Pittsburgh, PA
Super: @CMUTR