Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Color Vision01:24

Color Vision

1.9K
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.
1.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The impact of subchronic ozone exposure on serum metabolome and the mechanisms of abnormal bile acid and arachidonic acid metabolisms in the liver.

Ecotoxicology and environmental safety·2023
Same author

Elabela-APJ axis attenuates cerebral ischemia/reperfusion injury by inhibiting neuronal ferroptosis.

Free radical biology & medicine·2023
Same author

Fast response CdS-CdS<sub>x</sub>Te<sub>1-</sub><sub>x</sub>-CdTe core-shell nanobelt photodetector.

Science bulletin·2023
Same author

GmWAK1, Novel Wall-Associated Protein Kinase, Positively Regulates Response of Soybean to <i>Phytophthora sojae</i> Infection.

International journal of molecular sciences·2023
Same author

Optimized coaxial focused electrohydrodynamic jet printing of highly ordered semiconductor sub-microwire arrays for high-performance organic field-effect transistors.

Nanoscale·2023
Same author

Novel inhibition of AKR1C3 and androgen receptor axis by PTUPB synergizes enzalutamide treatment in advanced prostate cancer.

Oncogene·2023
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.8K

New adaptive color quantization method based on self-organizing maps.

Chip-Hong Chang1, Pengfei Xu, Rui Xiao

  • 1Center for High Performance Embedded Systems, Nanyang Technological University, Singapore 639798. echchang@ntu.edu.sg

IEEE Transactions on Neural Networks
|March 1, 2005
PubMed
Summary
This summary is machine-generated.

Frequency Sensitive Self-Organizing Maps (FS-SOMs) optimize color palettes for image processing, reducing artifacts. This competitive learning approach enhances image reconstruction quality and network robustness.

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.7K
Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
09:46

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

Published on: August 19, 2013

16.1K

Related Experiment Videos

Last Updated: Mar 24, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.8K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.7K
Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
09:46

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

Published on: August 19, 2013

16.1K

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Color quantization (CQ) reduces image colors for display, often causing artifacts.
  • Contouring artifacts arise from color reduction, impacting visual quality.

Purpose of the Study:

  • To introduce a novel competitive learning (CL) scheme, Frequency Sensitive Self-Organizing Maps (FS-SOMs), for optimized color palette design in CQ.
  • To minimize contouring artifacts and improve the visual quality of palletized images.

Main Methods:

  • FS-SOMs integrate neighborhood adaptation, frequency-sensitive learning, input randomization, and dead neuron reinitialization.
  • A most significant bit (MSB) biased encoding scheme is proposed to reduce parallel processing units.

Main Results:

  • FS-SOMs demonstrated superior performance over classical CL, LBG, and SOM algorithms in simulations.
  • The proposed method achieved better reconstruction quality and topological ordering with enhanced robustness.
  • The MSB biased encoding scheme allowed for network scaling without significant quality loss.

Conclusions:

  • FS-SOMs effectively optimize color palettes, reducing artifacts and improving image quality in CQ.
  • The FS-SOMs offer a robust and efficient solution for color quantization tasks.
  • The MSB biased encoding scheme presents a viable method for hardware acceleration in CQ.