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» Monte Carlo Localization Using SIFT Features
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ICCV
2007
IEEE
15 years 11 months ago
How Good are Local Features for Classes of Geometric Objects
Recent work in object categorization often uses local image descriptors such as SIFT to learn and detect object categories. Such descriptors explicitly code local appearance and h...
Michael Stark, Bernt Schiele
MM
2010
ACM
146views Multimedia» more  MM 2010»
14 years 9 months ago
Understanding the security and robustness of SIFT
Many content-based retrieval systems (CBIRS) describe images using the SIFT local features because of their very robust recognition capabilities. While SIFT features proved to cop...
Thanh-Toan Do, Ewa Kijak, Teddy Furon, Laurent Ams...
FLAIRS
2007
14 years 12 months ago
Exploiting MindStorms NXT: Mapping and Localization Projects for the AI Course
1 This paper describes two major student projects for the artificial intelligence course – Mapping using Bayesian filter and Monte Carlo Localization. These projects are also sui...
Myles F. McNally, Frank Klassner, Christopher Cont...
ECCV
2008
Springer
15 years 11 months ago
Image Feature Extraction Using Gradient Local Auto-Correlations
In this paper, we propose a method for extracting image features which utilizes 2 nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enab...
Takumi Kobayashi, Nobuyuki Otsu
ICCV
2005
IEEE
15 years 11 months ago
Local Features for Object Class Recognition
In this paper we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors / descriptors have been evaluated ...
Krystian Mikolajczyk, Bastian Leibe, Bernt Schiele