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CVPR
2008
IEEE
14 years 6 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
ECCV
2010
Springer
13 years 5 months ago
Object Recognition with Hierarchical Stel Models
Abstract. We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment ...
Alessandro Perina, Nebojsa Jojic, Umberto Castella...
ECCV
2002
Springer
14 years 6 months ago
Probabilistic Search for Object Segmentation and Recognition
Abstract. The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range ...
Ulrich Hillenbrand, Gerd Hirzinger
ACCV
2010
Springer
12 years 12 months ago
Continuous Surface-Point Distributions for 3D Object Pose Estimation and Recognition
We present a 3D, probabilistic object-surface model, along with mechanisms for probabilistically integrating unregistered 2.5D views into the model, and for segmenting model instan...
Renaud Detry, Justus H. Piater
ICCV
1995
IEEE
13 years 8 months ago
Recognition Using Region Correspondences
Recognition systems attempt to recover information about the identity of observed objects and their location in the environment. A fundamental problem in recognition is pose estima...
Ronen Basri, David W. Jacobs