Conference Information

DGMM 2027: International Conference on Discrete Geometry and Mathematical Morphology

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Submission Date:
2026-10-16 Closed
Notification Date:
2026-12-18
Conference Date:
2027-04-13
Location:
Grenoble, France
Years:
Viewed: 7829   Tracked: 0   Attend: 0
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Conference Partner Index (CP-I)

46.4 / 100
Ranked #2,581 of 5,768 conferences · Top 45%
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
48
Community attention (10%)
10
Public record completeness (15%)
55

Inputs used: Editions on record: 5 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-04

Call For Papers

DGMM 2027 (International Conference on Discrete Geometry and Mathematical Morphology) is an academic conference held in Grenoble, France on 2027-04-13. The paper submission deadline is 2026-10-16. Acceptance notifications are sent on 2026-12-18.

Topics Main topics of interest include (but are not limited to): models and algorithms for discrete geometry discrete shape representation, recognition and analysis geometric transforms, geometric estimators, discrete differential geometry tilings, discrete geometric spaces, unusual grids, packings discrete and combinatorial topology, discrete manifolds discrete tomography and convexity combinatorial, graph and computational geometry algebraic and morphological lattice theory nonlinear scale space theory hierarchical and graph based image models computational morphology morphological operators in machine learning discrete calculus and geometry processing geometric deep learning applications of discrete geometry and mathematical morphology (engineering, biomedical imaging, ...)
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