Kernel-based Matching

Kernel-Based Matching is a template matching technique that uses a kernel function to measure similarity between a template and image regions by mapping them into a higher-dimensional feature space, thereby enabling nonlinear pattern recognition through localized correlation in transformed domains.

134

Publications

9.1K

Citations

381

Authors

190

Institutions

Publications per year

2017–2026

27

Authors

381

  1. HJ
    Hervé Jeǵou

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

    5 publications · 459 citations

    5

    h-index

  2. GT
    Giorgos Tolias

    Czech Technical University in Prague, Czechia

    4 publications · 422 citations

    4

    h-index

  3. TD
    Trevor Darrell

    University of California, Berkeley, United States

    3 publications · 1.8K citations

    3

    h-index

  4. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    3 publications · 1.8K citations

    3

    h-index

  5. RK
    Ron Kimmel

    Technion – Israel Institute of Technology, Israel

    3 publications · 137 citations

    3

    h-index

1–5 of 381

Venues

  1. IEEE Transactions on Image Processing

    5 publications · 312 citations

    5

    h-index

  2. Pattern Recognition

    5 publications · 104 citations

    5

    h-index

  3. 5

    h-index

  4. 3

    h-index

  5. Computer Vision and Image Understanding

    CVIU · 3 publications · 93 citations

    3

    h-index

1–5