Feature Correspondence Analysis

Feature Correspondence Analysis is a methodological framework in computer vision and pattern recognition that establishes precise mappings between distinctive attributes or landmarks in paired or multi-view feature spaces to enable cross-domain, cross-modal, or cross-temporal data alignment.

161

Publications

7.1K

Citations

537

Authors

254

Institutions

Publications per year

2017–2026

37

Authors

537

  1. SC
    Shu‐Ching Chen

    Florida International University, United States

    6 publications · 180 citations

    6

    h-index

  2. MS
    Mei‐Ling Shyu

    University of Miami, United States

    5 publications · 163 citations

    5

    h-index

  3. JP
    Jean Ponce

    Institut national de recherche en sciences et technologies du numérique, France

    3 publications · 503 citations

    3

    h-index

  4. FB
    Francis Bach

    Institut national de recherche en sciences et technologies du numérique, France

    3 publications · 503 citations

    3

    h-index

  5. OD
    Olivier Duchenne

    Institut national de recherche en sciences et technologies du numérique, France

    3 publications · 503 citations

    3

    h-index

1–5 of 537

Venues

  1. Pattern Recognition

    5 publications · 223 citations

    5

    h-index

  2. PubMed

    Repository · 5 publications · 124 citations

    5

    h-index

  3. 3

    h-index

  4. Food Quality and Preference

    3 publications · 104 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 124 citations

    3

    h-index

1–5