Conference Information

EMMCVPR 2015: International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition

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EMMCVPR
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Submission Date:
2014-07-30 Extended
Notification Date:
2014-09-14
Conference Date:
2015-01-01
Location:
Hong Kong, China
Years:
10
Viewed: 20744   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

45.0 / 100
Ranked #2,746 of 5,651 conferences · Top 49%

#84 of 297 in Energy & Environment #138 of 241 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%)
65
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 10 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · 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 1.1 · Computed 2026-09-05

Call For Papers

EMMCVPR 2015 (International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition) is an academic conference held in Hong Kong, China on 2015-01-01. The paper submission deadline is 2014-07-30 (extended). Acceptance notifications are sent on 2014-09-14.

We welcome papers presenting original research, restricted to 14 pages Papers should be submitted to the conference site for double blind review Proceedings will be published by Springer in LNCS Papers Due: 15 07 2014. No later than 23.59 GMT. Methods of interest include but are not limited to Discrete and Continuous Optimization Convex Programming and Relaxations PDE and Variational Methods Stochastic Optimization Spectral Methods Graphical Models and Markov Random Fields Statistical Methods and Learning Application areas include but are not limited to Object Recognition and Detection Scene Understanding Segmentation and Grouping Restoration and Inpainting Color and Texture Computational Photography Photo and Video Editing Motion and Tracking Matching and Registration Medical Image Analysis Pose Estimation Action and Event Recognition Shape Analysis Shape-from-X Stereo and 3D Reconstruction Multiview Geometry Vision for Graphics
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