Mario Sormann

Computer Vision and Pattern Recognition · 2006

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

Whom Mario Sormann trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Pedro F. Felzenszwalb
  2. Vladimir Kolmogorov
  3. Dorin Comaniciu
  4. Harry Shum
  5. Li Hong
  6. Hai Tao
  7. Qingxiong Yang

Who trusts Mario Sormann overall

  1. Heiko Hirschmüller
  2. Andreas Geiger
  3. A. Ihler
  4. Koichiro Yamaguchi
  5. David A. McAllester
  6. Nalpantidis Lazaros
  7. Thibaut Weise
  8. Justin Domke
  9. Qingxiong Yang
  10. Shuangli Zhang

Papers from Mario Sormann's perspective

Ordered by how strongly the researchers Mario Sormann trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Efficient Belief Propagation for Early Vision (Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.)
  2. Computing visual correspondence with occlusions using graph cuts (Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001)
  3. Stereo Matching Using Belief Propagation (IEEE Transactions on Pattern Analysis and Machine Intelligence)
  4. Mean Shift: A Robust Approach Toward Feature Space Analysis
  5. A global matching framework for stereo computation (Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001)
  6. Symmetric stereo matching for occlusion handling (Computer Vision and Pattern Recognition)
  7. Stereo Matching with Color-Weighted Correlation, Hierachical Belief Propagation and Occlusion Handling (Computer Vision and Pattern Recognition)
  8. Segment-based stereo matching using graph cuts (Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.)
  9. Fast approximate energy minimization via graph cuts
  10. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

Their papers