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CVPR
2006
IEEE
16 years 6 months ago
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are pur...
Vidhya Navalpakkam, Laurent Itti
CVPR
2008
IEEE
16 years 6 months ago
Combining appearance models and Markov Random Fields for category level object segmentation
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider obje...
Diane Larlus, Frédéric Jurie
ECCV
2002
Springer
16 years 6 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
TEI
2010
ACM
268views Hardware» more  TEI 2010»
15 years 11 months ago
DisplayObjects: prototyping functional physical interfaces on 3d styrofoam, paper or cardboard models
This paper introduces DisplayObjects, a rapid prototyping workbench that allows functional interfaces to be projected onto real 3D physical prototypes. DisplayObjects uses a Vicon...
Eric Akaoka, Tim Ginn, Roel Vertegaal
ICB
2009
Springer
412views Biometrics» more  ICB 2009»
15 years 10 months ago
Bayesian Face Recognition Based on Markov Random Field Modeling
In this paper, a Bayesian method for face recognition is proposed based on Markov Random Fields (MRF) modeling. Constraints on image features as well as contextual relationships be...
Rui Wang, Zhen Lei, Meng Ao, Stan Z. Li