Shape context and chamfer matching in cluttered scenes

By Arasanathan Thayananthan, B. Stenger, Philip H. S. Torr, Roberto Cipolla. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003

374 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/77db36ebf6b6a48a7ffa881cbb7aa79d2988de0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

OBJ CUT (2005, background): “A point to point correspondence is established over the outline of a cow from a training video to the outlines of the horses using shape context with continuity constraint [20].”
Data fusion for visual tracking with particles (2004, background): “They can, however, be dramatically contaminated by clutter edges, even when detailed silhouette models are used [52].”
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016, methodology): “However, since U is ordered, this optimization can be solved efficiently using dynamic programming [54].”
Strike a pose: tracking people by finding stylized poses (2005, methodology): “Pr(Im(Pi)): We evaluate the local image likelihood with a chamfer template edge mask [24].”
Fast directional chamfer matching (2010, methodology): “Although proposed decades ago, chamfer matching [1] remains to be the preferred method when speed and accuracy are considered as discussed in [18].”
Embodied hands (2017, methodology): “…[Kim et al. 2012; Wang and Popović 2009], probabilistic line matching [Athitsos and Sclaroff 2003], reduced linear search sub-spaces [Heap and Hogg 1996; Wu et al. 2001], Bayesian filtering with Chamfer matching [Thayananthan et al. 2003], and physics-based methods [Melax et al. 2013].”
Shape Classification Using the Inner-Distance (2007, background): “Some examples can be found in [33], [43], [44], [47], [34], [26].”
A Survey of Shape Feature Extraction Techniques (2008, background): “It has been applied to a variety of object recognition problems [38, 39, 40, 41].”
Global Contrast Based Salient Region Detection (2015, methodology): “Classical shape methods such as shape contexts (SC) [55] and Chamfer Matching [59]”
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison (2007, methodology): “Unlike the above previous work, we focus on designing a distance metric for histogram-based local descriptors, which have attracted a lot of research interests recently [3], [32], [47], [48], [26], [29], [21], [34], [31].”
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation (2015, methodology): “Athitsos and Sclaroff (2003) resort to probabilistic line matching, while Thayananthan et al (2003) combine Bayesian filtering with Chamfer matching.”
Picture: A probabilistic programming language for scene perception (2015, methodology): “The representation layer ν in this program uses ﬁne-grained image contours [8] and the comparator is expressed as the probabilistic chamfer distance [41].”
3D Hand Shape and Pose From Images in the Wild (2019, background): “When it comes to a monocular color input, the problem becomes inherently ill posed due to the increased depth and scale ambiguities, but that did not prevent several researchers [4, 9, 51, 57, 63, 39] from attempting to solve it in the past albeit with limited results.”
Pedestrian detection in crowded scenes (2005, background): “tiple edge orientation planes, as proposed in [10, 22], but the general problem persists.”