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» Tracking Large Variable Numbers of Objects in Clutter
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ICIP
2005
IEEE
14 years 9 months ago
Multi-step active object tracking with entropy based optimal actions using the sequential Kalman filter
We describe an enhanced method for the selection of optimal sensor actions in a probabilistic state estimation framework. We apply this to the selection of optimal focal lengths f...
Benjamin Deutsch, Heinrich Niemann, Joachim Denzle...
CVPR
1999
IEEE
14 years 9 months ago
Object Recognition with Color Cooccurrence Histograms
We use the color cooccurrence histogram (CH) for recognizing objects in images. The color CH keeps track of the number of pairs of certain colored pixels that occur at certain sep...
Peng Chang, John Krumm
CVPR
2007
IEEE
14 years 9 months ago
A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence
We describe a method for fully automatic object recognition and segmentation using a set of reference images to specify the appearance of each object. Our method uses a generative...
Ian Simon, Steven M. Seitz
CVPR
2004
IEEE
14 years 9 months ago
Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting
We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter...
Fu Jie Huang, Léon Bottou, Yann LeCun
ECCV
2002
Springer
14 years 9 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic