Is HSL superior over HSI and HSV, because it takes human perception into account.?
For some image processing algorithms they say I can use either of these color spaces,
and I am not sure which one to pick. I mean, the algorithms just care that you provide
them with hue and saturation channel, you can pick which color space to use
Is HSL Superior over HSI and HSV Color Spaces
color-spacecolorscomputer visionimage processing
Related Solutions
You might want to check out http://www.cs.harvard.edu/~sjg/papers/cspace.pdf, which proposes a new colorspace apparently designed to address this precise question.
An alternative approach would be to extract features (keypoints) using the scale-invariant feature transform (SIFT) or Speeded Up Robust Features (SURF).
You can find a nice OpenCV code example in Java, C++, and Python on this page: Features2D + Homography to find a known object
Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle occlusion (as long as enough keypoints are visible).
Image source: tutorial example
The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.
Best Answer
Which one is best very much depends on what you're using it for. But in my experience HSL (HLS) has an unfortunate interaction between brightness and saturation.
Here's an example of reducing image brightness by 2. The leftmost image is the original; next comes the results using RGB, HLS, and HSV:
Notice the overly bright and saturated spots around the edge of the butterfly in HLS, particularly that red spot at the bottom. This is the saturation problem I was referring to.
This example was created in Python using the colorsys module for the conversions.