Methods › General › Geometric Matching › CHM

Convolutional Hough Matching

CHM

5 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Convolutional Hough Matching, or CHM, is a geometric matching algorithm that distributes similarities of candidate matches over a geometric transformation space and evaluates them in a convolutional manner. It is casted into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters.

Source: Convolutional Hough Matching Networks for Robust and...

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Adversarial Robustness1
Fine-Grained Image Classification1
Geometric Matching1
Image Classification1
Land Cover Classification1
Translation1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with CHM: 2021 to 2023, peak 3 3 0 2021: 1 paper 2021 2022: 3 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Geometric Matching

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections