In the interior of the G-Code a striking contrast between dark and light areas signals clarity. In addition to the stylistic appeal it exudes, there is also a technical reason behind this colour scheme. While black components such as the dirt-resistant floor covering or the carbon-fibre seat structure are supposed to visualise stability, durability or utility, the white interior components such as the soft leather upholstery on seats and armrests embody comfort and wellness. The high-contrast interior exudes modern luxury and at the same time provides the perfect stage for orchestrating the individual modes of the hybrid drive system. Similar to the visualisations of the display in the radiator grille, the interior lighting also varies, with the large wide-screen display adopting both the star matrix and the colour schemes.
First of all, you can define any colors you want to be the "primary colors" of your color system, so that other colors are obtained by mixing the primary colors. Although there may be an infinite number of color systems, they are not all equally useful, practical, or effective. For instance, I am free to create a color system where I define light blue, medium blue, and violet as my primary colors. Even though I am free to define my primary colors as such, this color system is not very useful in general because no amount of mixing of these primary colors will produce red, orange, yellow, etc. Therefore, we should make a distinction between a color system and an effective color system. The effectiveness of a color system is best measured as the number of different colors that can be created by mixing the primary colors of the system. This set of colors is called the "color gamut" of the system. A color system with a large gamut is more able to effectively represent a wide variety of images containing different colors.
Painting With Light And A Little G-Code
The most effective color systems are those that closely match the physical workings of the human eye, since it is ultimately the human eye which experiences the color. The human eye contains a curved array of light-sensing cells shaped like little cones and rods. Colored light is detected by the cone cells. The cone cells come in three varieties: red-detecting, green-detecting, and blue-detecting. They are so named because the red cone cells mostly detect red light, the green cone cells mostly detect green light, and the blue cone cells mostly detect blue light. Note that even though a red cone cell predominantly detects the color red, it can also detect a little bit of some other colors. Therefore, even though humans do not have yellow cone cells, we can still see yellow light when it triggers a red cone cell and a green cone cell. In this way, humans have a built-in color decoding mechanism which enables us to experience millions of colors, although we only have vision cells that predominantly see red, green, and blue. It should be obvious at this point that the most effective color systems are ones that closely match the human eye, i.e. color systems that mix red, green, and blue light.
In contrast to an additive system, color systems that remove colors through absorption are called "subtractive" color systems. They are called this because the final color is achieved by starting with white light (which contains all colors) and then subtracting away certain colors, leaving other colors. Examples of subtractive color systems are paints, pigments, and inks. An orange pumpkin that you see printed in a newspaper is not necessarily created by spraying orange ink on the paper. Rather, yellow ink and magenta ink are sprayed onto the paper. The yellow ink absorbs blue light and a little green and red from the white light beam, while the magenta ink absorbs green light and a little blue and red, leaving only orange to be reflected back.
For an additive system, light is created directly. This means that the primary colors of the most effective additive color system are simply red, green, and blue (RGB). This is why most computer screens, from iPods to televisions, contain a grid of little red-, green-, and blue-emitting light sources.
So where did the red-yellow-blue color system come from that they teach in elementary school? Typically, students first encounter color concepts when painting in an art class in grade school. Paint is a subtractive color system, and therefore the most effective primary colors for painting are cyan, magenta, and yellow. Note that high-quality paintings typically do not use just three primary colors since more vivid scenes can be achieved using dozens of primary colors. But when teaching art, it's easier to start more simply; with just three primary colors. Now, to a little grade-schooler, the words "cyan" and "magenta" don't mean much. Furthermore, to an undiscerning youngster's eye, cyan looks awfully close to blue and magenta looks awfully close to red. Therefore, cyan-magneta-yellow becomes corrupted to blue-red-yellow. Elementary art teachers either ignorantly perpetuate this less effective color model (because that's how they were taught as children), or intentionally perpetuate it (because it's just too hard to teach six-year-old's the difference between cyan and blue). Historical tradition was also a prime driver of the red-yellow-blue color system since it was historically thought to be effective before the details of human vision were understood. Since the red-yellow-blue color system is less effective, it is not really used anywhere these days except in elementary school art.
HSL (for hue, saturation, lightness) and HSV (for hue, saturation, value; also known as HSB, for hue, saturation, brightness) are alternative representations of the RGB color model, designed in the 1970s by computer graphics researchers to more closely align with the way human vision perceives color-making attributes. In these models, colors of each hue are arranged in a radial slice, around a central axis of neutral colors which ranges from black at the bottom to white at the top.
The HSL representation models the way different paints mix together to create color in the real world, with the lightness dimension resembling the varying amounts of black or white paint in the mixture (e.g. to create "light red", a red pigment can be mixed with white paint; this white paint corresponds to a high "lightness" value in the HSL representation). Fully saturated colors are placed around a circle at a lightness value of , with a lightness value of 0 or 1 corresponding to fully black or white, respectively.
Meanwhile, the HSV representation models how colors appear under light. The difference between HSL and HSV is that a color with maximum lightness in HSL is pure white, but a color with maximum value/brightness in HSV is analogous to shining a white light on a colored object (e.g. shining a bright white light on a red object causes the object to still appear red, just brighter and more intense, while shining a dim light on a red object causes the object to appear darker and less bright).
HSL and HSV are both cylindrical geometries (fig. 2), with hue, their angular dimension, starting at the red primary at 0, passing through the green primary at 120 and the blue primary at 240, and then wrapping back to red at 360. In each geometry, the central vertical axis comprises the neutral, achromatic, or gray colors ranging, from top to bottom, white at lightness 1 (value 1) to black at lightness 0 (value 0).
Brightness and colorfulness are absolute measures, which usually describe the spectral distribution of light entering the eye, while lightness and chroma are measured relative to some white point, and are thus often used for descriptions of surface colors, remaining roughly constant even as brightness and colorfulness change with different illumination. Saturation can be defined as either the ratio of colorfulness to brightness, or that of chroma to lightness.
All four of these leave the neutral axis alone. That is, for colors with R = G = B, any of the four formulations yields a lightness equal to the value of R, G, or B.
The HSI model commonly used for computer vision, which takes H2 as a hue dimension and the component average I ("intensity") as a lightness dimension, does not attempt to "fill" a cylinder by its definition of saturation. Instead of presenting color choice or modification interfaces to end users, the goal of HSI is to facilitate separation of shapes in an image. Saturation is therefore defined in line with the psychometric definition: chroma relative to lightness (fig. 15). See the Use in image analysis section of this article.[31]
Video editors also use these models. For example, both Avid and Final Cut Pro include color tools based on HSL or a similar geometry for use adjusting the color in video. With the Avid tool, users pick a vector by clicking a point within the hue/saturation circle to shift all the colors at some lightness level (shadows, mid-tones, highlights) by that vector.
For the most part, computer vision algorithms used on color images are straightforward extensions to algorithms designed for grayscale images, for instance k-means or fuzzy clustering of pixel colors, or canny edge detection. At the simplest, each color component is separately passed through the same algorithm. It is important, therefore, that the features of interest can be distinguished in the color dimensions used. Because the R, G, and B components of an object's color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult. Descriptions in terms of hue/lightness/chroma or hue/lightness/saturation are often more relevant.[31]
Starting in the late 1970s, transformations like HSV or HSI were used as a compromise between effectiveness for segmentation and computational complexity. They can be thought of as similar in approach and intent to the neural processing used by human color vision, without agreeing in particulars: if the goal is object detection, roughly separating hue, lightness, and chroma or saturation is effective, but there is no particular reason to strictly mimic human color response. John Kender's 1976 master's thesis proposed the HSI model. Ohta et al. (1980) instead used a model made up of dimensions similar to those we have called I, α, and β. In recent years, such models have continued to see wide use, as their performance compares favorably with more complex models, and their computational simplicity remains compelling.[S][31][39][40][41] 2ff7e9595c
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