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CVPR
2008
IEEE
16 years 8 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 ...
ICCV
2005
IEEE
16 years 8 months ago
Building a Classification Cascade for Visual Identification from One Example
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
ECCV
2004
Springer
16 years 8 months ago
Unifying Approaches and Removing Unrealistic Assumptions in Shape from Shading: Mathematics Can Help
This article proposes a solution of the Lambertian Shape From Shading (SFS) problem by designing a new mathematical framework based on the notion of viscosity solutions. The power ...
Emmanuel Prados, Olivier D. Faugeras
ECCV
2002
Springer
16 years 8 months ago
Learning Shape from Defocus
We present a novel method for inferring three-dimensional shape from a collection of defocused images. It is based on the observation that defocused images are the null-space of ce...
Paolo Favaro, Stefano Soatto
ICDE
2009
IEEE
156views Database» more  ICDE 2009»
16 years 8 months ago
Online Anomaly Prediction for Robust Cluster Systems
In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster opera...
Xiaohui Gu, Haixun Wang