Bayesian Nonparametric Intrinsic Image Decomposition

被引：0

作者：

Chang, Jason ^{[1
]}

Cabezas, Randi ^{[1
]}

Fisher, John W., III ^{[1
]}

机构：

[1] MIT, CSAIL, Cambridge, MA 02139 USA

来源：

COMPUTER VISION - ECCV 2014, PT IV | 2014年 / 8692卷

关键词：

Intrinsic images; Dirichlet process; Gaussian process; MCMC; SINGLE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a generative, probabilistic model that decomposes an image into reflectance and shading components. The proposed approach uses a Dirichlet process Gaussian mixture model where the mean parameters evolve jointly according to a Gaussian process. In contrast to prior methods, we eliminate the Retinex term and adopt more general smoothness assumptions for the shading image. Markov chain Monte Carlo sampling techniques are used for inference, yielding state-of-the-art results on the MIT Intrinsic Image Dataset.

引用

页码：704 / 719

页数：16

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