Human vision extracts the visible spectral component C*, called fundamental, from n-dimensional spectrum C. The projection from C to C* is described by the matrix R in FCS (Fundamental Color Space). FCS is spanned by a matrix F with
a selected triplet in R. The matrix R is decomposed into “achromatic” R_{A} and “chromatic” R_{C} by choosing matrix F.This paper presents a Luma/Chroma opponent-color space that is created from spectral decomposition
of fundamental based on matrix R theory. A new color space has orthogonal opponent-color axes with hue linearity because it's created through a linear naive transformation of fundamental in FCS.The key points lie in that the “chromatic” projector R_{C}
is further decomposed into R_{R} and R_{B} opponent-color components and an orthogonal Luma/Chroma FCS is newly created by a set of (R_{A}, R_{R}, R_{B}), each composed of n×n matrix. Now image colors are mapped
onto Luma/Chroma FCS. First, a tristimulus value XYZ from sRGB camera input is transformed back to the fundamental C* by pseudo-inverse projection. Next, C* is decomposed into the spectral triplet (C_{A}*, C_{R}*, C_{B}*)
through the (R_{A}, R_{R}, R_{B}). Finally, the achromatic fundamental C_{A}*(λ), n-dimensional vector, is converted to the luminance value LA by integral over λ. As well, the chromatic fundamentals, C_{R}*(λ)
and C_{B}*(λ) are converted to the chrominance values C_{R} and C_{B}. The paper shows how the image colors are mapped onto (L_{A}, C_{R}, C_{B}) Luma/Chroma space and introduces its application
to the image segmentation in comparison with conventional CIELAB and IPT color spaces.