TCS230颜色传感器的中英文翻译

基于TAOS公司的TCS230的颜色感应
TAOS公司的TCS230是一个小的 、高度集成、8引脚、SOIC封装的色彩传
感装置。它以模拟频率的方式输出短波（蓝色）、中波（绿 色）、长波（红色）、
宽带（白）光功率的事件数量。它可用于各种色彩感应应用领域。该设备的详细< br>资料中可以找到它的数据表。
我们将使用一个光学刺激方案的ColorChecker图表工 作，通过检测的色彩
数值例子。下图，在图1所示，是由GretagMacbeth生产和分配。图表 长约13
英寸，9英寸（330毫米×230毫米），它包含了64阵列安排24色斑。到5背
面图2显示了在图表的每一行四个补丁的光谱反射- 即入射光被反射的那部分（相
对于一个理想的漫反射）作为波长从350功能，纳米到750纳米。

图1 ColorChecker色补丁包含18个和6步灰色系列

图2 ColorChecker谱，第一行

图3 ColorChecker谱，第二排

图4 ColorChecker光谱，第三行

图5 ColorChecker谱，底排（中性系列）

图6锥锥光感受器敏感性所示。短波敏感的感光细胞远远低于其他两种类型的敏感。中
波和长波的感 光细胞的反应有很大的重叠。视觉是不敏感，准确的刺激波长：什么是光功率
下atters每个响应曲 线综合。
1. 色觉简介
所谓感光细胞在视网膜视锥细胞是人类色彩视觉负责。内有电磁频 谱三种类
型的视锥细胞，敏感的长波，中波，短波辐射及约400纳米之间和700纳米。由
于 锥敏感性在频谱的部分出现红色，绿色和蓝色的很粗糙，色彩科学家记为ρ，
γ，以及希腊字母为R，G 细胞的类型，和b （为了表示对传感器的R，G，和
B将错误建议更密切的对应关系。）的圆锥体的谱 反应的估计是在上面绘制图6。
在物理世界的光，其特征是光谱功率分布（结构化产品说明）。彩色对 象，
其特征是反射光谱曲线，如在的ColorChecker的。然而，视觉不敏感，对刺激
精确波长：根据现代色彩科学理论，最重要的事情是在每个响应曲线光功率积分。
这恰有三种视锥细胞类 型导致trichromaticity财产：三个组成部分是必要的和足
够的特征颜色。有些人可能会 用“感觉到的颜色的眼睛，“但我了CON - Sider
的限定词是多余的，充其量，误导在最坏的 情况：色彩是由视觉定义，所以没有
必要使用合格的短语“因为感觉到的眼睛，“或使用的形容词时可见 指颜色。
2. 色彩检查
如何光谱与颜色相关的知识装备，绘制色度坐标，对照明色彩的依 赖，我们
可以返回的ColorChecker。 GretagMac- Beth没公布或保证的C olorChecker补丁
的光谱成分。然而，标称Cie的[的X，Y和Z]值被公布。在底行的C olorChecker
补丁包含中性色，在图5中的神话传说中的数字符号反映十分之一的亮度（长* ）
值的这些补丁。
光谱绘制2和第3页上表示物理波长由波长的反射率的补丁。这些光谱反射
已测色仪测量tances称为分光光度计。如果你有机会访问光源具有完全的权力分
配，甚至 在整个可见光谱，反射率曲线则绘制在这里可以简单地扩展到repre，

发送应用程序 中的反射率。实践没有光源的光谱分布十分均匀，因此补偿neces-
萨利：你必须计算与图表的光谱 反射的光源的波长社民党按波长的产品。
我们将首先从图表计算在CIE[的X，Y和Z]值。 （这 些值应同意Gretag
提供的数字。）然后我们将计算[的R，G，B]的，将由一TCS230检测 值。
为了计算Cie公司[的X，Y和Z]，我们把31 ×3矩阵代表职能的配色在
CIE 标准观察者（CMFs），并执行一个有31个光谱响应矩阵产品价值为照明纠
正。这将产生的[x，Y ，Z轴]三刺激值。当色度坐标，通过投影[的x，y]是来自
[的X，Y和Z]变换计算公式1，然后 绘制，结果如图9色度图。马蹄状的人物，
在底部封闭，包含所有的颜色：每个非负的光谱分布产生[的 x，y]对本地区范围
内的阴谋。拥有轻成荫的三角显示包含所有的地区，可以通过一个附加的RGB< br>使用的sRGB系统（建议709）原色产生颜色。这个地区typifies视频和桌面计
算（ 的sRGB）。这些点绘制在图9是本的ColorChecker的颜色。白色和灰色值
都聚集在附近 的图表的中心。

图9 ColorChecker补丁坐标上绘制在CIE是[的x，y] 色度图。马蹄形包围了所有的颜色;
包围的三角形代表可以在视频（建议709）和桌面计算（的sRG B）的颜色。
3. TCS230
图10显示了TCS230的四个通道的反应。黑色曲线 显示了未经过滤的传感
器元件的响应。红色，绿色和蓝色的曲线显示了长波敏感，中波敏感，短波敏感< br>元素分别响应。
正如我在第5页提到，色觉Cie的模型，包含集成光栅一个在X（λ）和Y< br>（λ）和z（λ）配色函数行动（图7制成图表）社民党，生产X，Y和Z值。
要使用TCS23 0彩色估计我们执行了一个类似的计算，但使用而不是在CIE CMFs

TCS23 0灵敏度函数：我们整合下TCS230的灵敏度曲线社民党，生产的R，G
和B值。该设备的R，G和 B值将取决于几个因素：光源，样品的光谱反射光
谱的内容，任何干预光学元件的光谱衰减（如镜头）， 最后的光谱响应职能
TCS230。各种光谱现象为蓝本，通过计算波长的波长的产品。

图10 TCS230光谱灵敏度绘制在这里。红色，绿色和蓝色通道都绘制在相应的颜色;灰
线反映了清除（未过滤）通道的灵敏度。由于这些反应是从CIE标准观察者的不同，所报
告的值TCS 230没有色度。然而，适当的信号处理产生足够的颜色信息，对于许多工业应用
准确。
由于 事实TCS230是敏感的红外光（波长有700纳米以上），而事实上，大
多数光源产生的红外线地区 电力，典型应用包括一个红外截止在TCS230前过滤
器。背面图11显示了一个典型的红外滤光片的 反应。
继续我们的的ColorChecker造型，与我们照亮了CIE D65光源的的
ColorChecker，整合下的TCS230反射光谱敏感曲线产生，并最终转化为Cie的[的
x，y]坐标。相对亮度值，通过这个过程获得相当准确的，然而，染色体maticity
坐标不是很 准确。图12的裸图的R，G和B值在CIE色度。结果从不同的
ColorChecker坐标图9绘 制。
对于分歧的原因是TCS230的灵敏度函数系统蒸发散不同于匹配功能，是适
当的sR GB色彩了相当大。即使TCS230敏感性均符合的sRGB，在该光源的光
谱功率分布和干预光学康 波- nents会导致一些分歧谱的影响效果接近达成协议。
要形成一个色彩更准确地估计需要处理的原始TCS230的R，G，B值并通
过线性3 × 3矩阵的系数cients是相对于该光源，光学元件的干预光谱响应谱
优化，和响应曲线的TCS23 0。数据处理操作可以被表示为矩阵形式如下
：
x=M?t 公式 2
符号T表示一个三个元素的载体的设备价值从色块抓获。 M代表3 ×3色

校正矩阵，我们将适用于这些价值观通过矩阵乘法，由?符号表示。符号X表示
估计[的X，Y 和Z]值结果向量。
我们可以利用矩阵符号来象征加工三个色块安排一次设置成一个3×3矩阵
吨连续的T行连续列值的设备三套，包含红色，绿色和蓝色数据分别。经M矩
阵乘法，所产生的矩阵X 的列包含XYZ值的连续采样; X的行包含X，Y和Z
值分别tively。一个方程表达了三个补丁一次映射：
X=M?T 公式3
给定一个矩阵T的列包含三种器件样品集，并包含一个矩阵X某某三元三
组对应的理想 ，有一种独特的矩阵M，从T到X的映射是通过计算逆矩阵的T，
然后用X矩阵计算产品（由预乘）：
M=X ? 公式 4
由此产生的3 ×3色校正矩阵M的每一个选择三个值集的设备的Tris-
ti mulus值对应设置准确的地图。这是没有必要在反矩阵传感时间！矩阵M可以
事先计算，依据的预期 将提交拟申请在传感器的样品。要处理三对检测样品元件
值，所有这些都是必要的，是对矩阵乘积的计算 公式3。
一个色彩校正矩阵，产生在超过三个样本良好的效果，可以通过数值计算优
化过程。 当这样做，没有特别的样品可能正好映射到理想三原色集，但一个线性
矩阵可以构造，尽量减少跨样本范 围（其中的错误是在最小二乘意义上衡量）的
错误。色彩校正行动仍在完成公式2完全一样。

Sensing color with the TAOS TCS230
The TAOS TCS230 is a small, highly integrated color sensing device packaged
in a clear plastic 8-pin SOIC. It reports, as analog frequency, the amount of shortwave
(blue), mediumwave (green), longwave (red), and wideband (white) optical power
incident onto the device. It can be used in a variety of color sensing applications.
Details of the device can be found in its datasheet.
We will use the ColorChecker chart as an optical stimulus to work through a
numerical example of color sensing. The chart, depicted in Figure 1, is manufactured
and distributed by GretagMacbeth. The chart measures approximately 13 inches by 9
inches (330 mm by 230 mm); it contains 24 colored patches arranged in a 6 by 4 array.
Figures 2 through 5 overleaf show the spectral reflectance of the patches in each of
the four rows of the chart – that is, the fraction of incident light that is reflected (with
respect to an ideal diffuse reflector), as a function of wavelength from 350 nm to 750
nm.

Figure 1 The ColorChecker contains 18 colored patches and a 6-step gray series.

Figure 2 ColorChecker spectra, top row.

Figure 3 ColorChecker spectra, second row.

Figure 4 ColorChecker spectra, third row.

Figure 5 ColorChecker spectra, bottom row (neutral series)

Figure 6 Cone sensitivities of cone photoreceptors are shown. The shortwave-sensitive
photoreceptors are much less sensitive than the other two types. The responses of the
mediumwave and longwave photoreceptors have a great deal of overlap. Vision is not sensitive to
the precise wavelength of the stimulus: What atters is optical power integrated under each
response curve.

1. Introduction to color vision
Photoreceptor cells called cones in the retina are responsible for human color
vision. There are three types of cone cells, sensitive to longwave, mediumwave, and
shortwave radiation within the electro-magnetic spectrum between about 400 nm and
700 nm. Because the cone sensitivities are very roughly in the parts of the spectrum
that appear red, green, and blue, color scientists denote the cell types as ρ,γ, and , the
Greek letters for r, g, and b. (To denote the sensors R, G, and B would wrongly
suggest a closer correspondence.) Estimates of the spectral response of the cone types
are graphed in Figure 6 above.
Light in the physical world can be characterized by spectral power distributions
(SPDs). Colored objects can be characterized by spectral reflectance curves, such as
those of the ColorChecker. However, vision is insensitive to the exact wavelength of a
stimulus: According to the modern theory of color science, all that matters is the
integral of optical power underneath each response curve. That there are exactly three
types of cone cells leads to the property of trichromaticity: Three components are
necessary and sufficient to characterize color. Some people might use the phrase
“color as sensed by the eye,” but I con-sider that qualifier to be redundant at best, and
misleading at worst: Color is defined by vision, so there is no need to use the
qualifying phrase “as sensed by the eye,” or to use the adjective visible when

referring to color.
2. The Color Checker
Equipped with knowledge of how spectra are related to colors, the plotting of
chromaticity coordinates, and the dependence of colors upon illumination, we can
return to the ColorChecker. GretagMac-beth doesn’t publish or guarantee the spectral
composition of the patches of the ColorChecker. However, nominal CIE [X, Y, Z]
values are published. The patches in the bottom row of the ColorChecker contain
neutral colors; the numeric notations in the legends of Figure 5 reflect one tenth of the
lightness (L*) values of those patches.
The spectra graphed on pages 2 and 3 represent the physical
wave-length-by-wavelength reflectance of the patches. These spectral reflec- tances
have been measured by color measurement instrument called a spectrophotometer. If
you had access to a light source having perfectly even distribution of power across the
visible spectrum, then the reflectance curves graphed here could simply be scaled to
repre-sent the reflectance in your application. Practical light sources do not have
perfectly even spectral distributions, so compensation is neces-sary: You must
compute the wavelength-by-wavelength product of the illuminant’s SPD with the
spectral reflectance of the chart.
We will first calculate the CIE [X, Y, Z] values from the chart. (These values
should agree with the figures provided by Gretag.) Then we will calculate the [R, G, B]
values that will be detected by a TCS230.
To calculate CIE [X, Y, Z], we take the 31×3 matrix representing the color
matching functions (CMFs) of the CIE Standard Observer, and perform a matrix
product with 31 spectral response values as corrected for illumination. This produces
the [X, Y, Z] tristimulus values. When chromaticity coordinates [x, y] are computed
from [X, Y, Z] through the projective transform in Equation 1, then plotted, the
chromaticity diagram in Figure 9 results. The horseshoe-shaped figure, closed at the
bottom, contains all colors: Every non- negative spectral distribution produces an [x, y]
pair that plots within this region. The lightly-shaded triangle shows the region
containing all colors that can be produced by an additive RGB system using sRGB
(Rec. 709) primary colors. This region typifies video and desktop computing (sRGB).
The points plotted in Figure 9 are the colors of the ColorChecker. White and gray
values are clustered near the center of the chart.

Figure 9 Coordinates of ColorChecker patches are graphed on the CIE [x, y] chromaticity
diagram. The horseshoe encloses all colors; the triangle encloses the colors that can be represented
in video (Rec. 709) and in desktop computing (sRGB).
3. The TCS230
Figure 10 shows the responses of the four channels of the TCS230. The black
curve shows the response of the unfiltered sensor elements. The red, green, and blue
curves show the responses of the longwave-sensitive, mediumwave-sensitive, and
shortwave-sensitive elements respectively.
As I mentioned on page 5, the CIE model of color vision involves inte-grating an
SPD under the X(λ), Y(λ), and Z(λ) color matching func-tions (graphed in Figure 7),
producing X, Y, and Z values. To use the TCS230 to estimate color we perform an
analogous calculation, but using the TCS230 sensitivity functions instead of the CIE
CMFs: We integrate the SPD under the TCS230’s sensitivity curves, and produce R, G,
and B values. The device R, G, and B values will depend upon several factors: the
spectral content of the illuminant, the spectral reflectance of the sample, the spectral
attenuation of any intervening optical components (such as the lens), and finally, the
spectral response functions of the TCS230. The various spectral phenomena are
modelled by computing wavelength-by-wavelength products.

Figure 10 TCS230 spectral sensitivities are graphed here. The red, green, and blue channels
are graphed in the corresponding colors; the gray line reflects the sensitivity of the clear
(unfiltered) channel. Because these responses are different from the CIE standard observer, the
values reported by the TCS230 are not colorimetric. However, suitable signal processing yields
color information that is sufficiently accurate for many industrial applications.
Owing to the fact that the TCS230 is sensitive to infrared light (having
wavelengths above 700 nm), and the fact that most light sources

produce power in the
infrared region, typical applications include an IR cut filter in front of the TCS230.
Figure 11 overleaf shows the response of a typical IR cut filter.
To form a more accurate estimate of color requires processing the raw TCS230 R,
G, and B values through a linear 3×3 matrix whose coeffi-cients are optimized with
respect to the spectrum of the illuminant, the spectral response of intervening optical
components, and the

response curves of the TCS230. The data processing operation
can be represented in matrix form as follows:
x=M?t Eq 2
The symbol t represents a three-element vector containing the device values
captured from a color patch. M represents the 3×3 color correction matrix that we will
apply to these values through matrix multiplication, denoted by the ? symbol. The
symbol x represents the resulting vector of estimated [X, Y, Z] values.
We can use matrix notation to symbolize processing a set of three color patches
at once, by arranging the three sets of device values into successive columns of a 3×3
matrix T. Successive rows of T contain red, green, and blue data respectively. Upon
matrix multiplication by M, the columns of the resulting matrix X contain XYZ

values of the successive samples; the rows of X contain X, Y, and Z values
respec-tively. One equation expresses the mapping of three patches at once:
X=M?T Eq 3
Given a matrix T whose columns contain three sets of device samples, and a
matrix X containing the corresponding set of three ideal XYZ triples, there is a unique
matrix M that maps from T to X. It is found

by computing the matrix inverse of T,
then computing the matrix product (by premultiplication) with X:
M=X ? Eq 4
The resulting 3×3 color correction matrix M exactly maps the each of the chosen
three sets of device values to the corresponding set of tris-timulus values. It is not
necessary to invert matrices at the time of sensing! The matrix M can be computed in
advance, based upon the samples that are expected to be presented to the sensor in the
intended application. To process three device values upon sensing a sample, all that is
necessary is computation of the matrix product of Equation 3.
A color correction matrix that produces good results across more than three
samples can be computed through a numerical optimization procedure. When this is
done, no particular sample is likely to map exactly to its ideal tristimulus set, but a
linear matrix can be constructed that minimizes the error across a range of samples
(where the error is measured in a least- squares sense). The color correction operation
is still accomplished exactly as in Equation 2.