Sebastian Brutzer

Computer Vision and Pattern Recognition · 2011

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Sebastian Brutzer trusts overall

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

  1. Kyungnam Kim
  2. Jesse Davis
  3. Shireen Elhabian
  4. Thierry Bouwmans
  5. Thanarat Horprasert Chalidabhongse
  6. Andrea Prati
  7. David Harwood
  8. Mark H. Goadrich
  9. Massimo Piccardi
  10. Khaled M. El-Sayed

Who trusts Sebastian Brutzer overall

  1. Thierry Bouwmans
  2. Anestis Papazoglou
  3. Andrews Cordolino Sobral
  4. Yi Wang
  5. Kevin Hsieh
  6. Vittorio Ferrari
  7. Pierre-Marc Jodoin
  8. Fatih Murat Porikli
  9. Alon Faktor
  10. Ganesh Ananthanarayanan

Papers from Sebastian Brutzer's perspective

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

  1. Non-parametric Model for Background Subtraction (European Conference on Computer Vision)
  2. Real-time foreground-background segmentation using codebook model (Real-time imaging)
  3. Background subtraction techniques: a review (IEEE International Conference on Systems, Man and Cybernetics)
  4. The relationship between Precision-Recall and ROC curves
  5. Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art
  6. ViBE: A powerful random technique to estimate the background in video sequences (IEEE International Conference on Acoustics, Speech, and Signal Processing)
  7. Background modeling and subtraction by codebook construction (2004 International Conference on Image Processing, 2004. ICIP '04.)
  8. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications (IEEE Transactions on Image Processing)
  9. Detecting Moving Shadows: Algorithms and Evaluation (IEEE Transactions on Pattern Analysis and Machine Intelligence)
  10. Image Segmentation in Video Sequences: A Probabilistic Approach

Their papers