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Related Experiment Video

Updated: May 22, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Simultaneous cast shadows, illumination and geometry inference using hypergraphs.

Alexandros Panagopoulos1, Chaohui Wang, Dimitris Samaras

  • 1Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA. apanagop@cs.stonybrook.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 16, 2012
PubMed
Summary
This summary is machine-generated.

This study recovers scene illumination, geometry, and cast shadows from a single image using a novel framework. The method employs a higher order Markov Random Field (MRF) model for robust joint estimation.

Related Experiment Videos

Last Updated: May 22, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

  • Computer Vision
  • Computer Graphics
  • Artificial Intelligence

Background:

  • Cast shadows in images contain crucial information about scene illumination and geometry.
  • Existing methods often struggle with jointly estimating these properties from limited input.
  • Accurate scene understanding requires robust handling of light-environment interactions.

Purpose of the Study:

  • To develop a novel framework for jointly recovering illumination environment, geometry parameters, and cast shadows from a single image.
  • To leverage cast shadow information for improved scene understanding.
  • To enable simultaneous refinement of geometry and illumination estimation.

Main Methods:

  • Utilizing cast shadow information within a higher order Markov Random Field (MRF) illumination model.
  • Developing a method for obtaining approximate image evidence for cast shadows.
  • Employing a two-stage minimization technique for MRF energy inference, incorporating domain knowledge.
  • Jointly estimating illumination, geometry, and cast shadows using a single image and coarse 3D geometry.

Main Results:

  • The proposed framework successfully recovers illumination, geometry, and cast shadows.
  • The model demonstrates robustness to imprecise initial geometry and shadow estimates.
  • A generic coarse 3D model can be used for illumination estimation, or geometry can be refined simultaneously with illumination.

Conclusions:

  • Cast shadows are a valuable cue for joint illumination and geometry recovery.
  • The MRF-based approach provides a robust and effective solution for scene understanding.
  • The method offers flexibility in refining either geometry or illumination based on initial estimates.