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Recognizing Objects Using Color-Annotated Adjacency Graphs

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Shape, Contour and Grouping in Computer Vision

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1681))

Abstract

We introduce a new algorithm for identifying objects in clut- tered images, based on approximate subgraph matching. This algorithm is robust under moderate variations in the camera viewpoints. In other words, it is expected to recognize an object (whose model is derived from a template image) in a search image, even when the cameras of the template and search images are substantially different. The algorithm represents the objects in the template and search images by weighted adjacency graphs. Then the problem of recognizing the template object in the search image is reduced to the problem of approximately match- ing the template graph as a subgraph of the search image graph. The matching procedure is somewhat insensitive to minor graph variations, thus leading to a recognition algorithm which is robust with respect to camera variations.

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Authors and Affiliations

  1. GE - Corporate Research and Development, P.O.Box 8, Schenectady, NY, 12301, USA

    Peter Tu, Richard Hartley & Tushar Saxena

  2. CMA Consulting Services, Schenectady, NY, 12309, USA

    Tushar Saxena

Authors
  1. Peter Tu
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  2. Richard Hartley
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  3. Tushar Saxena
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© 1999 Springer-Verlag Berlin Heidelberg

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Tu, P., Hartley, R., Saxena, T. (1999). Recognizing Objects Using Color-Annotated Adjacency Graphs. In: Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science, vol 1681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46805-6_15

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  • DOI: https://doi.org/10.1007/3-540-46805-6_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66722-3

  • Online ISBN: 978-3-540-46805-9

  • eBook Packages: Springer Book Archive

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