Supervised Learning of a Color-Based Active Basis Model for Object Recognition

2010 Second International Conference on Knowledge and Systems Engineering ◽

10.1109/kse.2010.20 ◽

2010 ◽

Author(s):

T.T. Quyen Bui ◽

Keum-Shik Hong

Keyword(s):

Object Recognition ◽

Supervised Learning

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Supervised learning with potentials for neural network-based object recognition

Proceedings of 26th Southeastern Symposium on System Theory ◽

10.1109/ssst.1994.287885 ◽

2002 ◽

Author(s):

J.A. Starzyk ◽

Sinkuo Chai

Keyword(s):

Neural Network ◽

Object Recognition ◽

Supervised Learning

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Towards Underwater Object Recognition Based on Supervised Learning

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) ◽

10.1109/oceanskobe.2018.8559050 ◽

2018 ◽

Cited By ~ 1

Author(s):

Zhengyu Chen ◽

Tongtong Zhao ◽

Na Cheng ◽

Xundong Sun ◽

Xianping Fu

Keyword(s):

Object Recognition ◽

Supervised Learning ◽

Underwater Object

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Tactile Object Recognition with Semi-Supervised Learning

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-22876-1_2 ◽

2015 ◽

pp. 15-26 ◽

Cited By ~ 5

Author(s):

Shan Luo ◽

Xiaozhou Liu ◽

Kaspar Althoefer ◽

Hongbin Liu

Keyword(s):

Object Recognition ◽

Supervised Learning

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Semi-supervised learning and feature evaluation for RGB-D object recognition

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2015.05.007 ◽

2015 ◽

Vol 139 ◽

pp. 149-160 ◽

Cited By ~ 28

Author(s):

Yanhua Cheng ◽

Xin Zhao ◽

Kaiqi Huang ◽

Tieniu Tan

Keyword(s):

Object Recognition ◽

Supervised Learning ◽

Feature Evaluation

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Visual object recognition with supervised learning

IEEE Intelligent Systems ◽

10.1109/mis.2003.1200726 ◽

2003 ◽

Vol 18 (3) ◽

pp. 38-42 ◽

Cited By ~ 10

Author(s):

B. Heisele

Keyword(s):

Object Recognition ◽

Supervised Learning ◽

Visual Object ◽

Visual Object Recognition

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Semi-supervised Learning for RGB-D Object Recognition

2014 22nd International Conference on Pattern Recognition ◽

10.1109/icpr.2014.412 ◽

2014 ◽

Cited By ~ 20

Author(s):

Yanhua Cheng ◽

Xin Zhao ◽

Kaiqi Huang ◽

Tieniu Tan

Keyword(s):

Object Recognition ◽

Supervised Learning

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A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

Toward Category-Level Object Recognition - Lecture Notes in Computer Science ◽

10.1007/11957959_15 ◽

2006 ◽

pp. 277-300 ◽

Cited By ~ 4

Author(s):

Peter Carbonetto ◽

Gyuri Dorkó ◽

Cordelia Schmid ◽

Hendrik Kück ◽

Nando de Freitas

Keyword(s):

Object Recognition ◽

Supervised Learning ◽

Local Features ◽

Learning Approach ◽

Spatial Integration

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A supervised learning approach for fast object recognition from RGB-D data

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments - PETRA '14 ◽

10.1145/2674396.2674432 ◽

2014 ◽

Cited By ~ 1

Author(s):

David Paulk ◽

Vangelis Metsis ◽

Christopher McMurrough ◽

Fillia Makedon

Keyword(s):

Object Recognition ◽

Supervised Learning ◽

Learning Approach

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Self-supervised learning for object recognition based on kernel discriminant-EM algorithm

Proceedings Eighth IEEE International Conference on Computer Vision ICCV 2001 ICCV-01 ◽

10.1109/iccv.2001.937529 ◽

2002 ◽

Cited By ~ 15

Author(s):

Y. Wu ◽

T.S. Huang ◽

K. Toyama

Keyword(s):

Object Recognition ◽

Em Algorithm ◽

Supervised Learning

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Semi–supervised Learning with Constraints for Multi–view Object Recognition

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04277-5_66 ◽

2009 ◽

pp. 653-662 ◽

Cited By ~ 9

Author(s):

Stefano Melacci ◽

Marco Maggini ◽

Marco Gori

Keyword(s):

Object Recognition ◽

Supervised Learning

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