Color Theory in Computer Vision
Last Updated on January 6, 2023 by Editorial Team
Author(s): Akula Hemanth Kumar
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History of Color theory:
Color model from the book “Theory of Colors” by Johann Wolfgang.
Proof as to why RYB was Considered to be primary colors
- Also known as Young-Helmholtz theory.
Three types of cone photoreceptors
RGB Space Spanning
Opponent color theory
- There are colors not directly perceivable in normal lighting conditions.
- The trichromatic theory fails to incorporate these colors.
- The opponent-process works through excitatory and inhibitory responses, with the two components of each mechanism opposing each other.
- Monochromatic Single Channel
- Pixel values from 0(Black) to 255(White)
- Grayscale images contain only shades of gray.
- Less data complexity and storage requirements
- Many applications work well with gray scales. Complex channels are not required.
- Improved computation speeds.
- The additive color space is based on the RGB color model.
- Three Channels Red, Green, and Blue.
- Used in many image processing and computer vision applications.
Accessing channels of RGB image using OpenCV
Additive and Subtractive Color Theory
Convert RGB to CMYK using PIL
L -Lightness ( Intensity)
a -Green to Magenta
b -Blue to Yellow
Convert RGB to LAB using SkImage
Y-Luminance , Cr -Red, Cb -Blue
- Resemblance with YUV mode
Convert RGB to YCrCb using SkImage
Convert RGB to XYZ using SkImage
Convert RGB to HSV using Skimage
Other Color Spaces
- CIE-LCH color space
- YUV color space
- YIQ color space
- Stain color space
- HSL color space
You can find the complete jupyter notebook here.
If you have any questions, you can reach Abhishek. Feel free to reach out to him.
I am extremely passionate about computer vision and deep learning. I am an open-source contributor to Monk Libraries.
You can also see my other writings at:
Color Theory in Computer Vision was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
Published via Towards AI