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CVPR
2007
IEEE
15 years 11 months ago
Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts
This paper proposes a novel approach to constructing a hierarchical representation of visual input that aims to enable recognition and detection of a large number of object catego...
Sanja Fidler, Ales Leonardis
ABIALS
2008
Springer
15 years 3 months ago
A Neurocomputational Model of Anticipation and Sustained Inattentional Blindness in Hierarchies
Anticipation and prediction have been identified as key functions of many brain areas facilitating recognition, perception, and planning. In this chapter we present a hierarchical ...
Anthony F. Morse, Robert Lowe, Tom Ziemke
BCS
2008
14 years 11 months ago
Improved SIFT-Features Matching for Object Recognition
: The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully app...
Faraj Alhwarin, Chao Wang, Danijela Ristic-Durrant...
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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
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...