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» Learning Hierarchical Models of Scenes, Objects, and Parts
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ECCV
2010
Springer
15 years 2 months ago
Weakly Supervised Shape Based Object Detection with Particle Filter
Abstract. We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts ...
NIPS
2004
14 years 11 months ago
Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution
Capturing dependencies in images in an unsupervised manner is important for many image processing applications. We propose a new method for capturing nonlinear dependencies in ima...
Hyun-Jin Park, Te-Won Lee
CVPR
2011
IEEE
14 years 6 months ago
A Segmentation-aware Object Detection Model with Occlusion Handling
The bounding box representation employed by many popular object detection models [3, 6] implicitly assumes all pixels inside the box belong to the object. This assumption makes th...
Tianshi Gao, Benjamin Packer, Daphne Koller
RT
2001
Springer
15 years 2 months ago
Interactive Sampling and Rendering for Complex and Procedural Geometry
We present a new sampling method for procedural and complex geometries, which allows interactive point-based modeling and rendering of such scenes. For a variety of scenes, object-...
Marc Stamminger, George Drettakis
NIPS
2004
14 years 11 months ago
Contextual Models for Object Detection Using Boosted Random Fields
We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which use boos...
Antonio Torralba, Kevin P. Murphy, William T. Free...