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» Learning Models for Object Recognition
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IROS
2008
IEEE
113views Robotics» more  IROS 2008»
14 years 2 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
ECCV
2002
Springer
14 years 9 months ago
Probabilistic Search for Object Segmentation and Recognition
Abstract. The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range ...
Ulrich Hillenbrand, Gerd Hirzinger
CVPR
2011
IEEE
13 years 4 months ago
Learning to Share Visual Appearance for Multiclass Object Detection
We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the e...
Ruslan Salakhutdinov, Antonio Torralba, Josh Tenen...
ICCV
2007
IEEE
14 years 2 months ago
Support Kernel Machines for Object Recognition
Kernel classifiers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by...
Ankita Kumar, Cristian Sminchisescu
CLOR
2006
13 years 11 months ago
Object Recognition in the Geometric Era: A Retrospective
Recent advances in object recognition have emphasized the integration of intensity-derived features such as affine patches with associated geometric constraints leading to impressi...
Joseph L. Mundy