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» Learning Image Components for Object Recognition
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ICIAP
2005
ACM
16 years 2 months ago
Improving SIFT-Based Object Recognition for Robot Applications
In this article we proposed an improved SIFT-based object recognition methodology for robot applications. This methodology is employed for implementing a robot-head detection syste...
Patricio Loncomilla, Javier Ruiz-del-Solar
3DPVT
2006
IEEE
247views Visualization» more  3DPVT 2006»
15 years 6 months ago
Contour-Based Object Detection in Range Images
This paper presents a novel object recognition approach based on range images. Due to its insensitivity to illumination, range data is well suited for reliable silhouette extracti...
Stefan Stiene, Kai Lingemann, Andreas Nüchter...
ECCV
2004
Springer
16 years 4 months ago
Recognition by Probabilistic Hypothesis Construction
We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
Pierre Moreels, Michael Maire, Pietro Perona
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
15 years 8 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
DICTA
2009
15 years 3 months ago
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images
Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose fo...
Edmond Zhang, Michael Mayo