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» A Framework for Representing Moving Objects
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Publication
1301views
15 years 10 months ago
Markovian Tracking-by-Detection from a Single, Uncalibrated Camera
We present an algorithm for multi-person tracking-bydetection in a particle filtering framework. To address the unreliability of current state-of-the-art object detectors, our a...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
ICPR
2010
IEEE
15 years 1 months ago
Inverse Multiple Instance Learning for Classifier Grids
Abstract--Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if a...
Sabine Sternig, Peter M. Roth, Horst Bischof
PAMI
2010
190views more  PAMI 2010»
14 years 8 months ago
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues
—We present a probabilistic method for segmenting instances of a particular object category within an image. Our approach overcomes the deficiencies of previous segmentation tech...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
EDOC
2003
IEEE
15 years 3 months ago
MQL: a Powerful Extension to OCL for MOF Queries
The Meta-Object Facility (MOF) provides a standardised framework for object-oriented models. An instance of a MOF model contains objects and links whose interfaces are entirely de...
David Hearnden, Kerry Raymond, Jim Steel
ICIP
2009
IEEE
14 years 7 months ago
Shapes as empirical distributions
We address the problem of shape based classification. We interpret the shape of an object as a probability distribution governing the location of the points of the object. An imag...
Bernardo Rodrigues Pires, José M. F. Moura