A colorimetric modeling technique is proposed to give a computational model associated with colorimetry so that the representation of color acquired from camera imaging is accurate and meaningful. First of all, the camera spectral responses are estimated and the colorimetric quality is evaluated to reveal the feasibility of this work. In the modeling process, we present a spectral matching method and an approach of determining a reference-white luminance. As a result, the acquired color and the true (or measured) color can be well coordinated, with the strength of a global illumination or display white, in a perceptually uniform color space, e.g., in CIE 1976 L*a*b* space (abbreviated as CIELAB). Then, lower-degree polynomial regression is employed to eliminate color errors due to the mismatch between spectral response functions. Experimental results indicate that the root-mean-square ?E*ab value (i.e., color error) from the degree-3 polynomial regression is less than a just-noticeable difference (about 2.3) in CIELAB. It appears that the proposed technique can establish an accurate colorimetric model for vision systems.