Conference Information

SSVM 2027: International Conference on Scale Space and Variational Methods in Computer Vision

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Submission Date:
2026-12-20 Due in 58 days
Notification Date:
2027-02-28
Conference Date:
2027-06-06
Location:
Veldhoven, the Netherlands
Years:
Viewed: 18632   Tracked: 0   Attend: 0
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Conference Partner Index (CP-I)

50.3 / 100
Ranked #1,600 of 5,759 conferences · Top 28%

#75 of 246 in Computer Vision & Pattern Recognition

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%)
67
Community attention (10%)
12
Public record completeness (15%)
55

Inputs used: Editions on record: 11 · Researchers who opened this page in the past 24 months: 4

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 2026-10-23

Call For Papers

SSVM 2027 (International Conference on Scale Space and Variational Methods in Computer Vision) is an academic conference held in Veldhoven, the Netherlands on 2027-06-06. The paper submission deadline is 2026-12-20. Acceptance notifications are sent on 2027-02-28.

Topics Submissions to this conference cover a wide variety of intersection points between mathematics and imaging. Have a look at the proceedings of previous editions. A selection of topics includes: 3D vision Approximation methods in imaging Compressed sensing Convex and nonconvex optimization Cross-scale structure Differential geometry and invariants Flow matching and optimal transport Geometry and metric learning from generative models Geometric learning Image registration Image- and feature analysis Imaging modalities Implicit surfaces Inverse problems in imaging Machine learning in imaging Manifold-valued data processing Mathematics of novel imaging methods Medical imaging and other applications Motion estimation and tracking Multi-orientation and multi-scale modelling and analysis Optical flow Optimization methods in imaging PDEs in data processing Perceptual grouping Restoration and reconstruction Scale-space methods Segmentation Selection of salient scales Shape from X Stereo and multi-view reconstruction (Sub-)Riemannian geometry Surface and shape modelling and analysis Variational methods
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