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DICTA
2009
13 years 4 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
SCIA
2005
Springer
186views Image Analysis» more  SCIA 2005»
13 years 9 months ago
Building Detection from Mobile Imagery Using Informative SIFT Descriptors
We propose reliable outdoor object detection on mobile phone imagery from off-the-shelf devices. With the goal to provide both robust object detection and reduction of computation...
Gerald Fritz, Christin Seifert, Manish Kumar, Luca...
ICML
2006
IEEE
14 years 4 months ago
Active sampling for detecting irrelevant features
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo...
ICPR
2006
IEEE
14 years 4 months ago
Trains of keypoints for 3D object recognition
This paper presents a 3D object recognition method that exploits the spatio-temporal coherence of image sequences to capture the object most relevant features. We start from an im...
Elise Arnaud, Francesca Odone, Alessandro Verri
CVPR
2010
IEEE
13 years 7 months ago
Harvesting Large-Scale Weakly-Tagged Image Databases from the Web
To leverage large-scale weakly-tagged images for computer vision tasks (such as object detection and scene recognition), a novel cross-modal tag cleansing and junk image filtering...
Jianping Fan