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Improved Metric Space for Shape Correspondence

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Computer Vision and Image Processing (CVIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2009))

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Abstract

Shape correspondence is a fundamental task of finding a map among the elements of a pair of shapes. Particularly, non-rigid shapes add to the challenge of computing correspondences as they have their respective metric structures. In order to establish a mapping between non-rigid shapes, it is necessary to bring them into a common metric space. The idea is to identify shape forms that are invariant to isometric deformations and are embedded in a Euclidean space. These pose-invariant features are then aligned to identify point-to-point correspondences. Geodesic distances have been utilized to compute these shape-invariant forms. However, these distances are quite sensitive to topological noise present in the shape. This work proposes to overcome these challenges by utilizing shape-aware distances to identify invariant forms that are unaffected by topological variations of the shape and are smoother than geodesic distance. These distances along with the non-rigid alignment of shape forms in the Euclidean domain led to an improved point-to-point correspondence, enabling it to work effectively, even when dealing with different triangulations of the shape.

Supported by Visvesvaraya PhD Scheme, MeitY, Government of India.

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Acknowledgement

This work was financially supported by Visvesvaraya PhD Scheme, Ministry of Electronics and Information Technology, Government of India under Grant MEITY-PHD-1090.

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Authors and Affiliations

  1. Goa University, Goa, India

    Manika Bindal

  2. Indian Institute of Technology (IIT) Goa, Goa, India

    Venkatesh Kamat

Authors
  1. Manika Bindal
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  2. Venkatesh Kamat
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Corresponding author

Correspondence to Manika Bindal .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology, Jammu, India

    Harkeerat Kaur

  2. Indian Institute of Technology, Jammu, India

    Vinit Jakhetiya

  3. Indian Institute of Technology, Ropar, India

    Puneet Goyal

  4. Indian Institute of Information Technology, Jabalpur, India

    Pritee Khanna

  5. Indian Institute of Technology, Roorkee, Uttarakhand, India

    Balasubramanian Raman

  6. Indian Institute of Technology, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Bindal, M., Kamat, V. (2024). Improved Metric Space for Shape Correspondence. In: Kaur, H., Jakhetiya, V., Goyal, P., Khanna, P., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2023. Communications in Computer and Information Science, vol 2009. Springer, Cham. https://doi.org/10.1007/978-3-031-58181-6_32

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  • DOI: https://doi.org/10.1007/978-3-031-58181-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-58180-9

  • Online ISBN: 978-3-031-58181-6

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