Abstract
AbstractThe task of reconstructing camera motion and the shapes of objects from multiple images is a fundamental and important one in computer vision. Although among the reconstruction methods, the batch processing factorization method proposed by Tomasi and Kanade gives mathematically stable and good reconstruction results, it requires computational time that increases proportionately with the number of images handled and thus cannot be easily applied to real‐time processing. This paper proposes a recursive factorization method for a metric affine projection model, which allows a factorization method to be applied to real‐time processing. The proposed method makes it possible to minimize the sizes of the matrices handled and to shorten the computational time significantly while realizing a reconstruction accuracy comparable to that of motion matrices, by lossless compression of the motion information of the motion matrices by means of principal component analyses. In addition, the proposed method facilitates the processing of occlusion points and the addition of feature points. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(7): 83–93, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10134
Published Version
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