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» Model generation for video-based object recognition
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IROS
2008
IEEE
113views Robotics» more  IROS 2008»
13 years 11 months ago
Motion recognition and generation by combining reference-point-dependent probabilistic models
— This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned usin...
Komei Sugiura, Naoto Iwahashi
MVA
1998
154views Computer Vision» more  MVA 1998»
13 years 6 months ago
Clustering of Learning Images and Generation of Multiple Prototypes for Object Recognition
common features in all learning objects only. The In this paper, we propose two methods of clustering learning images to generate prototypes automatically for object recognition. O...
Jin Jia, Keiichi Abe
AR
2011
13 years 11 days ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
FGR
2000
IEEE
150views Biometrics» more  FGR 2000»
13 years 9 months ago
From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-b...
Athinodoros S. Georghiades, Peter N. Belhumeur, Da...
IJCV
2000
164views more  IJCV 2000»
13 years 5 months ago
Probabilistic Modeling and Recognition of 3-D Objects
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
Joachim Hornegger, Heinrich Niemann