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Related Experiment Video

Updated: Feb 17, 2026

Multimodal Optical Imaging Platform for Studying Cellular Metabolism
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Image processing pipeline for segmentation and material classification based on multispectral high dynamic range

Miguel Ángel Martínez-Domingo, Eva M Valero, Javier Hernández-Andrés

    Optics Express
    |December 10, 2017
    PubMed
    Summary

    We developed a new method for capturing high dynamic range (HDR), multispectral (MS), and polarimetric (Pol) images. This automated process improves object segmentation and classification for indoor scenes.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Optical Engineering

    Background:

    • Capturing high dynamic range (HDR), multispectral (MS), and polarimetric (Pol) images of indoor scenes presents significant challenges.
    • Existing methods often lack automation and robust pre-processing for accurate image registration.

    Purpose of the Study:

    • To propose an automated method for capturing MSHDRPol images using a liquid crystal tunable filter (LCTF).
    • To develop pre-processing techniques for accurate registration of HDR images, crucial for polarization analysis.
    • To enhance object segmentation and classification, particularly distinguishing between metal and dielectric materials.

    Main Methods:

    • Utilized a liquid crystal tunable filter (LCTF) for image acquisition.
    • Implemented adaptive exposure estimation (AEE) for automated capture.
    • Developed a pre-processing method for HDR image registration and alignment of polarization data.
    • Employed mean shift, cluster averaging, and region merging for object segmentation.
    • Incorporated degree of linear polarization (DoLP) maps for material classification.

    Main Results:

    • The proposed method successfully captures MSHDRPol images of indoor scenes.
    • The automated pipeline streamlines the image acquisition and pre-processing.
    • Segmentation performance was evaluated against Ncut and Watershed methods.
    • Classification using DoLP information from highlight and surrounding regions showed improved accuracy.

    Conclusions:

    • The developed image processing pipeline offers superior performance compared to existing techniques for MSHDRPol image cubes.
    • The method provides a robust framework for object segmentation and classification in complex indoor environments.
    • This work advances the capabilities of MSHDRPol imaging for detailed scene analysis.