Efficient shape matching using shape contexts
By Greg Mori, Serge J. Belongie, Jitendra Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
456 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/83c26bdfa1981b1bd8632258d4efedf3ba06910f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Alexander C. Berg
- Jitendra Malik
- Eli Shechtman
- Michal Irani
- Oren Boiman
- Greg Mori
- Pietro Perona
- Piotr Doll'ar
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Bernt Schiele
- Jitendra Malik
- Xiaogang Wang
- Dacheng Tao
- Trevor Darrell
- Ming-Hsuan Yang
- Andrew Zisserman
Citations in the corpus
- In defense of Nearest-Neighbor based image classification (2008, methodology): “Shape descriptor: We extended the Shape-Context descriptor [22] to contain edge-orientation histograms in”
- Recovering 3D human body configurations using shape contexts (2006, methodology): “In particular, we use therepresentative shape contexts pruning algorithm [38] to construct this shortlist of candidate exemplars.”
- Effective and Efficient Global Context Verification for Image Copy Detection (2017, methodology): “Inspired by shape context [35], [36], Mortensen et al.”
- The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, background): “For instance, SIFT [71], [72] and other local descriptors [73], [74], [75] have been intensively used in the domain of object recognition and image retrieval [76], [77], [78] for decades as they are invariant to scaling, orientation and illumination changes.”
- Pedestrian Detection: An Evaluation of the State of the Art (2012, background): “Wojek and Schiele [M ULTI F TR ] [56] showed how a combination of Haar-like features, shapelets [33], shape context [57] and HOG features outperforms any individual feature.”
- Shape Matching and Object Recognition (2006, methodology): “In addition the mathematical development showing that geometric blur is a method to obtain robustness to small affine transforms has been applied to understanding of a shape contexts, and has inspired some of the generalization of shape contexts [Mori et al., 2005].”