↓ Skip to main content

Energy Minimization Methods in Computer Vision and Pattern Recognition

Overview of attention for book
Cover of 'Energy Minimization Methods in Computer Vision and Pattern Recognition'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Rapid Mode Estimation for 3D Brain MRI Tumor Segmentation
  3. Altmetric Badge
    Chapter 2 Energy Minimization Methods in Computer Vision and Pattern Recognition
  4. Altmetric Badge
    Chapter 3 Linear Osmosis Models for Visual Computing
  5. Altmetric Badge
    Chapter 4 Analysis of Bayesian Blind Deconvolution
  6. Altmetric Badge
    Chapter 5 A Variational Method for Expanding the Bit-Depth of Low Contrast Image
  7. Altmetric Badge
    Chapter 6 Energy Minimization Methods in Computer Vision and Pattern Recognition
  8. Altmetric Badge
    Chapter 7 Simultaneous Fusion Moves for 3D-Label Stereo
  9. Altmetric Badge
    Chapter 8 Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints
  10. Altmetric Badge
    Chapter 9 Discrete Geodesic Regression in Shape Space
  11. Altmetric Badge
    Chapter 10 Object Segmentation by Shape Matching with Wasserstein Modes
  12. Altmetric Badge
    Chapter 11 Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data
  13. Altmetric Badge
    Chapter 12 An Optimal Control Approach to Find Sparse Data for Laplace Interpolation
  14. Altmetric Badge
    Chapter 13 Curvature Regularization for Resolution-Independent Images
  15. Altmetric Badge
    Chapter 14 PoseField: An Efficient Mean-Field Based Method for Joint Estimation of Human Pose, Segmentation, and Depth
  16. Altmetric Badge
    Chapter 15 Semantic Video Segmentation from Occlusion Relations within a Convex Optimization Framework
  17. Altmetric Badge
    Chapter 16 A Co-occurrence Prior for Continuous Multi-label Optimization
  18. Altmetric Badge
    Chapter 17 Convex Relaxations for a Generalized Chan-Vese Model
  19. Altmetric Badge
    Chapter 18 Multiclass Segmentation by Iterated ROF Thresholding
  20. Altmetric Badge
    Chapter 19 A Generic Convexification and Graph Cut Method for Multiphase Image Segmentation
  21. Altmetric Badge
    Chapter 20 Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs
  22. Altmetric Badge
    Chapter 21 Contour-Relaxed Superpixels
  23. Altmetric Badge
    Chapter 22 Sparse-MIML: A Sparsity-Based Multi-Instance Multi-Learning Algorithm
  24. Altmetric Badge
    Chapter 23 Consensus Clustering with Robust Evidence Accumulation
  25. Altmetric Badge
    Chapter 24 Variational Image Segmentation and Cosegmentation with the Wasserstein Distance
  26. Altmetric Badge
    Chapter 25 A Convex Formulation for Global Histogram Based Binary Segmentation
  27. Altmetric Badge
    Chapter 26 A Continuous Shape Prior for MRF-Based Segmentation
Attention for Chapter 8: Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
15 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints
Chapter number 8
Book title
Energy Minimization Methods in Computer Vision and Pattern Recognition
Published in
Lecture notes in computer science, August 2013
DOI 10.1007/978-3-642-40395-8_8
Book ISBNs
978-3-64-240394-1, 978-3-64-240395-8
Authors

Thomas Möllenhoff, Claudia Nieuwenhuis, Eno Töppe, Daniel Cremers

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 27%
Student > Master 3 20%
Student > Doctoral Student 2 13%
Student > Ph. D. Student 2 13%
Student > Bachelor 1 7%
Other 1 7%
Unknown 2 13%
Readers by discipline Count As %
Computer Science 7 47%
Engineering 2 13%
Mathematics 2 13%
Chemical Engineering 1 7%
Unknown 3 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2014.
All research outputs
#18,385,510
of 22,772,779 outputs
Outputs from Lecture notes in computer science
#6,005
of 8,124 outputs
Outputs of similar age
#148,239
of 198,506 outputs
Outputs of similar age from Lecture notes in computer science
#144
of 191 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 198,506 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 191 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.