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» Learning Mid-Level Features For Recognition
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IROS
2007
IEEE
134views Robotics» more  IROS 2007»
15 years 10 months ago
Feature selection for grasp recognition from optical markers
Abstract— Although the human hand is a complex biomechanical system, only a small set of features may be necessary for observation learning of functional grasp classes. We explor...
Lillian Y. Chang, Nancy S. Pollard, Tom M. Mitchel...
ICML
2010
IEEE
15 years 5 months ago
A Theoretical Analysis of Feature Pooling in Visual Recognition
Many modern visual recognition algorithms incorporate a step of spatial `pooling', where the outputs of several nearby feature detectors are combined into a local or global `...
Y-Lan Boureau, Jean Ponce, Yann LeCun
ICDAR
2003
IEEE
15 years 9 months ago
A Flexible Recognition Engine for Complex On-line Handwritten Character Recognition
A major feature of new mobiles terminals using penbased interfaces, such as personal assistants or e-book, is their personal character, implying that a good interface should be ea...
Sanparith Marukatat, Rudy Sicard, Thierry Arti&egr...
EMNLP
2009
15 years 2 months ago
Semi-supervised Speech Act Recognition in Emails and Forums
In this paper, we present a semi-supervised method for automatic speech act recognition in email and forums. The major challenge of this task is due to lack of labeled data in the...
Minwoo Jeong, Chin-Yew Lin, Gary Geunbae Lee
HCI
2009
15 years 2 months ago
Sign Language Recognition: Working with Limited Corpora
The availability of video format sign language corpora limited. This leads to a desire for techniques which do not rely on large, fully-labelled datasets. This paper covers various...
Helen Cooper, Richard Bowden