Supervised learning of hidden and non-hidden 0-order affordances and detection in real scenes
By Aitor Aldoma, Federico Tombari, Markus Vincze. 2012
41 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Hema Swetha Koppula
- Rudhir Gupta
- Ashutosh Saxena
- Yiannis Aloimonos
- Ching Lik Teo
- Cornelia Fermüller
- Austin Myers
- Yun Jiang
In Computer Vision and Pattern Recognition
- Ashutosh Saxena
- Luc Van Gool
- Kristen Grauman
- Hema Swetha Koppula
- Jitendra Malik
- Li Fei-Fei
- Trevor Darrell
- Cordelia Schmid
In Control and Systems Engineering
- Ashutosh Saxena
- Hema Swetha Koppula
- Rudhir Gupta
- Yiannis Aloimonos
- Cornelia Fermüller
- Ching Lik Teo
- Austin Myers
- Kristen Grauman
Citations in the corpus
- Learning human activities and object affordances from RGB-D videos (2012, methodology): “Aldoma et al. (2012) proposed a method to find affordances which depends solely on the objects of interest and their position and orientation in the scene.”
- Affordance detection of tool parts from geometric features (2015, methodology): “In contrast to previous works that require accurate metric models [2] or predict attributes for segmented objects [35], we show that local geometric primitives are sufficient for pixel accurate functionality detection compared to those discovered via deep learning (which returns only a bounding box)”