TSL color space
TSL color space (
Conversion between RGB and TSL
The conversion from gamma-corrected
- – the zero special case is to maintain the original behavior
- – the Luma
where:
- – the rg chromaticity
- – centering on white
Likewise, the reverse transform is as follows:[2]
where:
- – Luma converted to average intensity
For T = 0, conversion from TSL to RGB is not unique because the sign of r' is lost by the forward conversion due to the g' = 0 special case. Removing the special case produces a system that deviates from the original paper but preserves the sign.
Advantages of TSL
The advantages of TSL color space lie within the normalization within the RGB-TSL transform. Utilizing normalized r and g allows for chrominance spaces TSL to be more efficient for skin color segmentation. Additionally with this normalization, the sensitivity of the chrominance distributions to the variability of skin color is significantly reduced, allowing for an easier detection of different skin tones.[3]
Comparison of TSL to other color spaces
Terrillon investigated the efficiency of facial detection for several different color spaces. Testing consisted of using the same algorithm with 10 different color spaces to detect faces in 90 images with 133 faces and 59 subjects - 27 Asian, 31 Caucasian, and 1 African). TSL showed superior performance to the other spaces, with 90.8% correct detection and 84.9% correct rejection. A full comparison can be seen in the table below.[3]
Color Space | # of Elements | CD (%) | CR (%) |
---|---|---|---|
TSL | 258 | 90.8 | 84.9 |
r-g | 328 | 74.6 | 80.3 |
CIE-xy | 388 | 56.6 | 83.5 |
CIE-DSH | 318 | 60.9 | 75.0 |
HSV | 408 | 55.7 | 84.7 |
YIQ | 471 | 47.3 | 79.8 |
YES | 494 | 41.6 | 80.3 |
CIELUV | 418 | 24.1 | 79.0 |
CIELAB |
399 | 38.4 | 83.6 |
Disadvantages of TSL
TSL space could be made more efficient and robust. There currently exists no color correction algorithms for different camera systems. Additionally, despite a better accuracy of skin tone detection, detecting dark skin color still proves to be a challenge.[1]
Applications
Being a relatively new color space and having very specific uses, TSL hasn’t been widely implemented. Again, it is only very useful in skin detection algorithms. Skin detection itself can be used for a variety of applications – face detection, person tracking (for
See also
References
- ^ a b c Terrillon, Jean-Christophe; Akamatsu, Shigeru (1998). Automatic Detection of Human Faces in Natural Scene Images by Use of a Skin Color Model and of Invariant Moments. Proc. Of the Third International Conference on Automatic Face and Gesture Recognition. Nara, Japan. pp. 130–135.
- ^ Dmitry Ivanov (21 June 2023). "Color-space: tsl.js". GitHub.
- ^ S2CID 39824480.
- ^ Brown, D.; Craw, I.; Lewthwaite, J. (2001). A SOM Based Approach to Skin Detection with Application in Real Time Systems. British Machine Vision Conference. Manchester, United Kingdom.