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» Object of Interest Detection by Saliency Learning
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ICCV
2005
IEEE
15 years 3 months ago
Learning Hierarchical Models of Scenes, Objects, and Parts
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
71
Voted
DAGM
2005
Springer
15 years 3 months ago
Goal-Directed Search with a Top-Down Modulated Computational Attention System
In this paper we present VOCUS: a robust computational attention system for goal-directed search. A standard bottom-up architecture is extended by a top-down component, enabling th...
Simone Frintrop, Gerriet Backer, Erich Rome
85
Voted
ECCV
2006
Springer
15 years 11 months ago
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
This paper introduces the Located Hidden Random Field (LHRF), a conditional model for simultaneous part-based detection and segmentation of objects of a given class. Given a traini...
Ashish Kapoor, John M. Winn
ICCV
2009
IEEE
15 years 3 months ago
Joint Pose Estimator and Feature Learning for Object Detection
A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, an...
Karim Ali, Francois Fleuret, David Hasler and Pasc...
CVPR
2007
IEEE
15 years 11 months ago
Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention
A critical function in both machine vision and biological vision systems is attentional selection of scene regions worthy of further analysis by higher-level processes such as obj...
Robert J. Peters, Laurent Itti