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2003 JMLR JMLR 2003

A Generative Model for Separating Illumination and Reflectance from Images

Abstract

It is well known that even slight changes in nonuniform illumination lead to a large image variability and are crucial for many visual tasks. This paper presents a new ICA related probabilistic model where the number of sources exceeds the number of sensors to perform an image segmentation and illumination removal, simultaneously. We model illumination and reflectance in log space by a generalized autoregressive process and Hidden Gaussian Markov random field, respectively. The model ability to deal with segmentation of illuminated images is compared with a Canny edge detector and homomorphic filtering. We apply the model to two problems: synthetic image segmentation and sea surface pollution detection from intensity images. [abs] [ pdf ][ ps.gz ][ ps ]

🌱 Topic Pioneer — Image Restoration
🌉 Interdisciplinary Bridge — Computer Vision and Machine Learning
📈 Trend Setter — Stochastic Processes
🧭 Keyword Pioneer — image segmentation
🐣 Hot Topic Early Bird — generative model
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Inna Stainvas , David Lowe

Topics

Machine Learning > Optimization & Theory > Stochastic Processes Computer Vision > Processing > Image Restoration Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Computer Vision > Processing > Image Processing

Keywords

image segmentation independent component analysis generative model markov random field autoregressive model illumination removal reflectance separation illumination separation
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