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Hsv abgänge

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Furthermore, different RGB displays use different primaries, and so have different gamuts. If we take an image and extract the hue, saturation, and lightness or value components, and then compare these to the components of the same name as defined by color scientists, we can quickly see the difference, perceptually.

For example, examine the following images of a fire breather fig. The original is in the sRGB colorspace. Though none of the dimensions in these spaces match their perceptual analogs, the value of HSV and the saturation of HSL are particular offenders.

Such perversities led Cynthia Brewer, expert in color scheme choices for maps and information displays, to tell the American Statistical Association:.

Computer science offers a few poorer cousins to these perceptual spaces that may also turn up in your software interface, such as HSV and HLS.

Perceptual color dimensions are poorly scaled by the color specifications that are provided in these and some other systems.

For example, saturation and lightness are confounded, so a saturation scale may also contain a wide range of lightnesses for example, it may progress from white to green which is a combination of both lightness and saturation.

These flaws make the systems difficult to use to control the look of a color scheme in a systematic manner. If much tweaking is required to achieve the desired effect, the system offers little benefit over grappling with raw specifications in RGB or CMY.

If these problems make HSL and HSV problematic for choosing colors or color schemes, they make them much worse for image adjustment.

HSL and HSV, as Brewer mentioned, confound perceptual color-making attributes, so that changing any dimension results in non-uniform changes to all three perceptual dimensions, and distorts all of the color relationships in the image.

In the example below fig. Notice how the hue-shifted middle version without such a correction dramatically changes the perceived lightness relationships between colors in the image.

The creators of HSL and HSV were far from the first to imagine colors fitting into conic or spherical shapes, with neutrals running from black to white in a central axis, and hues corresponding to angles around that axis.

Similar arrangements date back to the 18th century, and continue to be developed in the most modern and scientific models.

First, we compute chroma, by multiplying saturation by the maximum chroma for a given lightness or value. Next, we find the point on one of the bottom three faces of the RGB cube which has the same hue and chroma as our color and therefore projects onto the same point in the chromaticity plane.

Finally, we add equal amounts of R , G , and B to reach the proper lightness or value. Then we can find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.

Finally, we can find R , G , and B by adding the same amount to each component, to match lightness:.

First, we find chroma:. Then we can, again, find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.

Finally, we can find R , G , and B by adding the same amount to each component, to match value:. Then we can find R , G , and B by adding the same amount to each component, to match luma:.

Mouse over the swatches below to see the R , G , and B values for each swatch in a tooltip. From Wikipedia, the free encyclopedia.

Alternative representations of the RGB color model. If we plot hue and a HSL lightness or b HSV value against chroma range of RGB values rather than saturation chroma over maximum chroma for that slice , the resulting solid is a bicone or cone , respectively, not a cylinder.

Such diagrams often claim to represent HSL or HSV directly, with the chroma dimension confusingly labelled "saturation".

Painters long mixed colors by combining relatively bright pigments with black and white. Mixtures with white are called tints , mixtures with black are called shades , and mixtures with both are called tones.

See Tints and shades. This color model by German chemist Wilhelm Ostwald exemplifies the "mixtures with white and black" approach, organizing 24 "pure" colors into a hue circle , and colors of each hue into a triangle.

The model thus takes the shape of a bicone. The same image, with a portion removed for clarity. Tektronix graphics terminals used the earliest commercial implementation of HSL, in This diagram, from a patent filed in , shows the bicone geometry underlying the model.

Color picker and Image editing. Computer vision and Image analysis. Notice that the lines pointing to the red, green, and blue primaries are not evenly spaced by hue angle, and are of unequal length.

They carefully and unambiguously described and compared three models: Unfortunately, later authors were less fastidious, and current usage of these terms is inconsistent and often misleading.

Other definitions commonly use integer values in the range [0, ] , storing the value for each component in one byte.

We define the RGB gamut to be a unit cube for convenience, because it simplifies and clarifies the math. We will drop the primes, and the labels R , G , and B should be taken to stand for the three attributes of the origin RGB space, whether or not it is gamma corrected.

Our formulas which follow are some mix of those. See also RGB color space Specifications. Many of these screenshots were taken from the GUIdebook , and the rest were gathered from image search results.

Creates a result color with the hue and saturation of the base color and the luminance of the blend color.

Die Harmonie der Farben. Communications of the ACM. ACM Transactions on Graphics. Digital Color Imaging Handbook. Gonzalez and Richard Eugene Woods Digital Image Processing , 3rd ed.

Upper Saddle River, NJ: David Baron July Environmental Systems Research Institute. Retrieved August 30, Archived from the original PDF on Computer Graphics and Image Processing.

International Journal of Computer Vision. Proceedings of the Section on Statistical Graphics. Statistical Analysis of Circular Data.

Circular Statistics Applied to Colour Images. List of color spaces Color models. Retrieved from " https: Articles with short description Wikipedia articles needing clarification from June Views Read Edit View history.

In other projects Wikimedia Commons. This page was last edited on 6 November , at By using this site, you agree to the Terms of Use and Privacy Policy.

HSV type 2 primarily causes urogenital infections and is found almost exclusively in adults. HSV type 1 is closely associated with orolabial infection, although genital infection with this virus can be common in certain populations.

The diagnosis of HSV infections is routinely made based on clinical findings and supported by laboratory testing using PCR or viral culture.

There are several circumstances in which it may be important to distinguish between infection caused by HSV types 1 and 2.

In addition, the results of HSV type-specific IgG testing is sometimes used during pregnancy to identify risks of congenital HSV disease and allow for focused counseling prior to delivery.

The presence of IgG-class antibodies to HSV types 1 or 2 indicates previous exposure, and does not necessarily indicate that HSV is the causative agent of an acute illness.

Individuals infected with herpes simplex virus HSV may not exhibit detectable levels of IgM antibody in the early stages of infection.

For patients presenting with presumed acute infection with HSV, a clinical specimen eg, oral, dermal, or genital lesion should be sampled and submitted for detection of HSV types 1 and 2 by rapid PCR: Serum specimens collected too early in the course of infection may not have detectable levels of HSV IgG.

In cases of suspected early disease, a repeat serum specimen should be collected 14 to 21 days later and submitted for testing. The presence of IgG-class antibodies to either HSV type 1 or 2 does not differentiate between remote infection and acute disease.

The predictive value of positive or negative results depends on the prevalence of disease and the pretest likelihood of HSV-1 and HSV False-positive results may occur.

Repeat testing, or testing by a different method, may be indicated in some settings eg, patients with low likelihood of HSV infection.

Ashley RL, Wald A: Clin Microbiol Rev ; Premarket evaluation of a commercial glycoprotein G-based enzyme immunoassay for herpes simplex virus type-specific antibodies.

J Clin Microbiol ; The acquisition of herpes simplex virus during pregnancy. N Engl J Med ;

Thus if we add or subtract the same amount from all three of R , G , and B , we move vertically within our tilted cube, and do not change the projection.

For points which project onto the origin in the chromaticity plane i. Mathematically, this definition of hue is written piecewise: Sometimes, neutral colors i.

These definitions amount to a geometric warping of hexagons into circles: After such a transformation, hue is precisely the angle around the origin and chroma the distance from the origin: The atan2 function, a "two-argument arctangent", computes the angle from a cartesian coordinate pair.

Notice that these two definitions of hue H and H 2 nearly coincide, with a maximum difference between them for any color of about 1.

The two definitions of chroma C and C 2 differ more substantially: While the definition of hue is relatively uncontroversial—it roughly satisfies the criterion that colors of the same perceived hue should have the same numerical hue—the definition of a lightness or value dimension is less obvious: Here are four of the most common fig.

All four of these leave the neutral axis alone. The creators of these models considered this a problem for some uses. For example, in a color selection interface with two of the dimensions in a rectangle and the third on a slider, half of that rectangle is made of unused space.

Now imagine we have a slider for lightness: To solve problems such as these, the HSL and HSV models scale the chroma so that it always fits into the range [0, 1] for every combination of hue and lightness or value, calling the new attribute saturation in both cases fig.

To calculate either, simply divide the chroma by the maximum chroma for that value or lightness. The HSI model commonly used for computer vision, which takes H 2 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: See the Use in image analysis section of this article.

Using the same name for these three different definitions of saturation leads to some confusion, as the three attributes describe substantially different color relationships; in HSV and HSI, the term roughly matches the psychometric definition, of a chroma of a color relative to its own lightness, but in HSL it does not come close.

Even worse, the word saturation is also often used for one of the measurements we call chroma above C or C 2. The original purpose of HSL and HSV and similar models, and their most common current application, is in color selection tools.

At their simplest, some such color pickers provide three sliders, one for each attribute. Most, however, show a two-dimensional slice through the model, along with a slider controlling which particular slice is shown.

Several color choosers from the s are shown to the right, most of which have remained nearly unchanged in the intervening time: Some more sophisticated variants are designed for choosing whole sets of colors, basing their suggestions of compatible colors on the HSL or HSV relationships between them.

Most web applications needing color selection also base their tools on HSL or HSV, and pre-packaged open source color choosers exist for most major web front-end frameworks.

HSL and HSV are sometimes used to define gradients for data visualization , as in maps or medical images. Image editing software also commonly includes tools for adjusting colors with reference to HSL or HSV coordinates, or to coordinates in a model based on the "intensity" or luma defined above.

In particular, tools with a pair of "hue" and "saturation" sliders are commonplace, dating to at least the lates, but various more complicated color tools have also been implemented.

For instance, the Unix image viewer and color editor xv allowed six user-definable hue H ranges to be rotated and resized, included a dial -like control for saturation S HSV , and a curves -like interface for controlling value V —see fig.

Video editors also use these models. These have been copied widely, but several imitators use the HSL e.

The applications of such tools include object detection, for instance in robot vision ; object recognition , for instance of faces , text , or license plates ; content-based image retrieval ; and analysis of medical images.

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.

Starting in the late s, 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: 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.

While HSL, HSV, and related spaces serve well enough to, for instance, choose a single color, they ignore much of the complexity of color appearance.

Essentially, they trade off perceptual relevance for computation speed, from a time in computing history high-end s graphics workstations, or mids consumer desktops when more sophisticated models would have been too computationally expensive.

If we plot the RGB gamut in a more perceptually-uniform space, such as CIELAB see below , it becomes immediately clear that the red, green, and blue primaries do not have the same lightness or chroma, or evenly spaced hues.

Furthermore, different RGB displays use different primaries, and so have different gamuts. If we take an image and extract the hue, saturation, and lightness or value components, and then compare these to the components of the same name as defined by color scientists, we can quickly see the difference, perceptually.

For example, examine the following images of a fire breather fig. The original is in the sRGB colorspace.

Though none of the dimensions in these spaces match their perceptual analogs, the value of HSV and the saturation of HSL are particular offenders.

Such perversities led Cynthia Brewer, expert in color scheme choices for maps and information displays, to tell the American Statistical Association:.

Computer science offers a few poorer cousins to these perceptual spaces that may also turn up in your software interface, such as HSV and HLS. Perceptual color dimensions are poorly scaled by the color specifications that are provided in these and some other systems.

For example, saturation and lightness are confounded, so a saturation scale may also contain a wide range of lightnesses for example, it may progress from white to green which is a combination of both lightness and saturation.

These flaws make the systems difficult to use to control the look of a color scheme in a systematic manner. If much tweaking is required to achieve the desired effect, the system offers little benefit over grappling with raw specifications in RGB or CMY.

If these problems make HSL and HSV problematic for choosing colors or color schemes, they make them much worse for image adjustment.

HSL and HSV, as Brewer mentioned, confound perceptual color-making attributes, so that changing any dimension results in non-uniform changes to all three perceptual dimensions, and distorts all of the color relationships in the image.

In the example below fig. Notice how the hue-shifted middle version without such a correction dramatically changes the perceived lightness relationships between colors in the image.

The creators of HSL and HSV were far from the first to imagine colors fitting into conic or spherical shapes, with neutrals running from black to white in a central axis, and hues corresponding to angles around that axis.

Similar arrangements date back to the 18th century, and continue to be developed in the most modern and scientific models.

First, we compute chroma, by multiplying saturation by the maximum chroma for a given lightness or value. Next, we find the point on one of the bottom three faces of the RGB cube which has the same hue and chroma as our color and therefore projects onto the same point in the chromaticity plane.

Finally, we add equal amounts of R , G , and B to reach the proper lightness or value. Then we can find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.

Finally, we can find R , G , and B by adding the same amount to each component, to match lightness:.

First, we find chroma:. Then we can, again, find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.

Finally, we can find R , G , and B by adding the same amount to each component, to match value:. Then we can find R , G , and B by adding the same amount to each component, to match luma:.

Mouse over the swatches below to see the R , G , and B values for each swatch in a tooltip. From Wikipedia, the free encyclopedia.

Handsfield or our other experts to answer? Visit Ask the Experts to learn how. The accurate herpes blood tests detect IgG antibodies.

A recent study corroborates this finding: The challenge here is that the time it takes for IgG antibodies to reach detectable levels can vary from person to person.

For one person, it could take just a few weeks, while it could ta ke a few months for another. So even with the accurate tests, a person could receive a false negative if the test is taken too soon after contracting the virus.

For the most accurate test result, it is recommended to wait 12 — 16 weeks from the last possible date of exposure before getting an accurate, type-specific blood test in order to allow enough time for antibodies to reach detectable levels.

There are currently several FDA-approved, gG-based blood tests that can give accurate results for herpes. Like any blood test, these tests cannot determine whether the site of infection is oral or genital.

However, since most cases of genital herpes are caused by HSV-2, a positive result for type-2 antibodies most likely indicates genital herpes.

The resource explains the increasing role of type-specific herpes serologic assays, presents clinical scenarios in which serologic testing are beneficial, and reviews key factors in a differential diagnosis for genital herpes.

Testing with symptoms How does herpes testing work? Check out the infographic to learn more. Testing with no symptoms Blood tests can be used when a person has no visible symptoms but has concerns about having herpes.

IgG When an individual contracts herpes, the immune system responds by developing antibodies to fight the virus: IgM tests are not recommended because of three serious problems: Many assume that if a test discovers IgM, they have recently acquired herpes.

However, research shows that IgM can reappear in blood tests in up to a third of people during recurrences, while it will be negative in up to half of persons who recently acquired herpes but have culture-document first episodes.

Therefore, IgM tests can lead to deceptive test results, as well as false assumptions about how and when a person actually acquired HSV.

For this reason, we do not recommend using blood tests as a way to determine how long a person has had herpes. Unfortunately, most people who are diagnosed will not be able to determine how long they have had the infection.

This is important in that most of the adult population in the U.

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