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Related Concept Videos

Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

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Related Experiment Video

Updated: May 21, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Algorithm validation using multicolor phantoms.

Daniel V Samarov, Matthew L Clarke, Ji Youn Lee

    Biomedical Optics Express
    |June 29, 2012
    PubMed
    Summary
    This summary is machine-generated.

    We introduce Spatial LASSO (SPLASSO), a novel algorithm for hyperspectral image analysis. SPLASSO improves abundance fraction estimation by incorporating spatial information, outperforming standard LASSO on a new dye mixture dataset.

    Keywords:
    (000.5490) Probability theory, stochastic processes, and statistics(110.4234) Multispectral and hyperspectral imaging(120.0120) Instrumentation, measurement, and metrology(170.0170) Medical optics and biotechnology(170.3880) Medical and biological imaging(180.0180) Microscopy(350.4800) Optical standards and testing

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    Last Updated: May 21, 2026

    Cross-Modal Multivariate Pattern Analysis
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    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
    07:34

    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    Area of Science:

    • Biomedical optics
    • Image analysis
    • Spectroscopy

    Background:

    • Hyperspectral imaging (HSI) is crucial for analyzing complex mixtures.
    • Estimating abundance fractions of components in HSI is challenging.
    • Existing methods like LASSO offer sparse representations but often neglect spatial information.

    Purpose of the Study:

    • To introduce and validate a novel algorithm, Spatial LASSO (SPLASSO), for hyperspectral image analysis.
    • To establish a new benchmark dataset using water-soluble dye mixtures on microarray chips for HSI validation.
    • To demonstrate the superiority of SPLASSO over the standard LASSO in abundance fraction estimation.

    Main Methods:

    • Development of the Spatial LASSO (SPLASSO) algorithm, extending the LASSO framework.
    • Utilizing a custom-designed microarray chip platform with water-soluble dye mixtures for HSI data acquisition.
    • Comparative performance analysis of SPLASSO and LASSO algorithms for abundance fraction estimation.

    Main Results:

    • SPLASSO effectively incorporates spatial information into abundance estimation.
    • The novel dye mixture dataset serves as a valuable benchmark for HSI analysis.
    • SPLASSO demonstrates improved accuracy in abundance fraction estimation compared to the standard LASSO.

    Conclusions:

    • Spatial LASSO (SPLASSO) offers a significant advancement in hyperspectral image analysis.
    • The proposed framework and dataset facilitate robust validation of HSI algorithms.
    • SPLASSO's integration of spatial context enhances the precision of abundance estimation in HSI.