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seorang pemungut sampah

Seorang Pemungut Sampah yang Bekerja Keras dengan Seluruh Tenaganya untuk Mencari Uang Supaya Dapat Menghidupi Keluarganya
Dia Sangat Berjasa, Dimata Orang-orang yang Bekerja Dikantoran Mereka adalah Orang Kecil 
Tetapi Sebenernya Seorang Pemungut Sampah Lebih Tinggi Dari Pada Orang Yang Berkerja Dikantoran
Seorang Pemungut Sampah Sangat Berjasa Bagi Semua Orang 
Dia Orang Yang telah Membersihkan Sampah-sampah yang telah dibuang oleh Kita.. 
Selama Ini Kita hanya membuang sampah sembarangan..  

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"MERAPI" secrets of nature, the source of destruction.

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white balance

AWhite balance (WB) is the process of removing unrealistic color casts, so that objects which appear white in person are rendered white in your photo. Proper camera white balance has to take into account the "color temperature" of a light source, which refers to the relative warmth or coolness of white light. Our eyes are very good at judging what is white under different light sources, but digital cameras often have great difficulty with auto white balance (AWB) — and can create unsightly blue, orange, or even green color casts. Understanding digital white balance can help you avoid these color casts, thereby improving your photos under a wider range of lighting conditions.

Example of an incorrect white balanceExample with corrected white balance
Incorrect White BalanceCorrect White Balance

Color temperature describes the spectrum of light which is radiated from a "blackbody" with that surface temperature. A blackbody is an object which absorbs all incident light — neither reflecting it nor allowing it to pass through. A rough analogue of blackbody radiation in our day to day experience might be in heating a metal or stone: these are said to become "red hot" when they attain one temperature, and then "white hot" for even higher temperatures. Similarly, blackbodies at different temperatures also have varying color temperatures of "white light." Despite its name, light which may appear white does not necessarily contain an even distribution of colors across the visible spectrum:


Relative intensity has been normalized for each temperature (in Kelvins).
Note how 5000 K produces roughly neutral light, whereas 3000 K and 9000 K produce light spectrums which shift to contain more orange and blue wavelengths, respectively. As the color temperature rises, the color distribution becomes cooler. This may not seem intuitive, but results from the fact that shorter wavelengths contain light of higher energy.
Why is color temperature a useful description of light for photographers, if they never deal with true blackbodies? Fortunately, light sources such as daylight and tungsten bulbs closely mimic the distribution of light created by blackbodies, although others such as fluorescent and most commercial lighting depart from blackbodies significantly. Since photographers never use the term color temperature to refer to a true blackbody light source, the term is implied to be a "correlated color temperature" with a similarly colored blackbody. The following table is a rule-of-thumb guide to the correlated color temperature of some common light sources:
Color TemperatureLight Source
1000-2000 K Candlelight
2500-3500 K Tungsten Bulb (household variety)
3000-4000 K Sunrise/Sunset (clear sky)
4000-5000 K Fluorescent Lamps
5000-5500 K Electronic Flash
5000-6500 K Daylight with Clear Sky (sun overhead)
6500-8000 K Moderately Overcast Sky
9000-10000 K Shade or Heavily Overcast Sky

Since some light sources do not resemble blackbody radiators, white balance uses a second variable in addition to color temperature: the green-magenta shift. Adjusting the green-magenta shift is often unnecessary under ordinary daylight, however fluorescent and other artificial lighting may require significant green-magenta adjustments to the WB.
 Auto White Balance
Custom
Kelvin
Tungsten
Fluorescent
Daylight
Flash
Cloudy
Shade
Fortunately, most digital cameras contain a variety of preset white balances, so you do not have to deal with color temperature and green-magenta shift during the critical shot. Commonly used symbols for each of these are listed to the left.
The first three white balances allow for a range of color temperatures. Auto white balance is available in all digital cameras and uses a best guess algorithm within a limited range — usually between 3000/4000 K and 7000 K. Custom white balance allows you to take a picture of a known gray reference under the same lighting, and then set that as the white balance for future photos. With "Kelvin" you can set the color temperature over a broad range.
The remaining six white balances are listed in order of increasing color temperature, however many compact cameras do not include a shade white balance. Some cameras also include a "Fluorescent H" setting, which is designed to work in newer daylight-calibrated fluorescents.
The description and symbol for the above white balances are just rough estimates for the actual lighting they work best under. In fact, cloudy could be used in place of daylight depending on the time of day, elevation, or degree of haziness. In general, if your image appears too cool on your LCD screen preview (regardless of the setting), you can quickly increase the color temperature by selecting a symbol further down on the list above. If the image is still too cool (or warm if going the other direction), you can resort to manually entering a temperature in the Kelvin setting.
If all else fails and the image still does not have the correct WB after inspecting it on a computer afterwards, you can adjust the color balance to remove additional color casts. Alternatively, one could click on a colorless reference (see section on neutral references) with the "set gray point" dropper while using the "levels" tool in Photoshop. Either of these methods should be avoided since they can severely reduce the bit depth of your image.

By far the best white balance solution is to photograph using the RAW file format (if your camera supports them), as these allow you to set the WB *after* the photo has been taken. RAW files also allow one to set the WB based on a broader range of color temperature and green-magenta shifts.
Performing a white balance with a raw file is quick and easy. You can either adjust the temperature and green-magenta sliders until color casts are removed, or you can simply click on a neutral reference within the image (see next section). Even if only one of your photos contains a neutral reference, you can click on it and then use the resulting WB settings for the remainder of your photos (assuming the same lighting).

A neutral reference is often used for color-critical projects, or for situations where one anticipates auto white balance will encounter problems. Neutral references can either be parts of your scene (if you're lucky), or can be a portable item which you carry with you. Below is an example of a fortunate reference in an otherwise bluish twilight scene.
On the other hand, pre-made portable references are almost always more accurate since one can easily be tricked into thinking an object is neutral when it is not. Portable references can be expensive and specifically designed for photography, or may include less expensive household items. An ideal gray reference is one which reflects all colors in the spectrum equally, and can consistently do so under a broad range of color temperatures. An example of a pre-made gray reference is shown below:
Common household neutral references are the underside of a lid to a coffee or pringles container. These are both inexpensive and reasonably accurate, although custom-made photographic references are the best (such as the cards shown above). Custom-made devices can be used to measure either the incident or reflected color temperature of the illuminant. Most neutral references measure reflected light, whereas a device such as a white balance meter or an "ExpoDisc" can measure incident light (and can theoretically be more accurate).
Care should be taken when using a neutral reference with high image noise, since clicking on a seemingly gray region may actually select a colorful pixel caused by color noise:
Low Noise
(Smooth Colorless Gray)
High Noise
(Patches of Color)
If your software supports it, the best solution for white balancing with noisy images is to use the average of pixels with a noisy gray region as your reference. This can be either a 3x3 or 5x5 pixel average if using Adobe Photoshop.

Certain subjects create problems for a digital camera's auto white balance — even under normal daylight conditions. One example is if the image already has an overabundance of warmth or coolness due to unique subject matter. The image below illustrates a situation where the subject is predominantly red, and so the camera mistakes this for a color cast induced by a warm light source. The camera then tries to compensate for this so that the average color of the image is closer to neutral, but in doing so it unknowingly creates a bluish color cast on the stones. Some digital cameras are more susceptible to this than others.
Automatic White BalanceCustom White Balance
(Custom white balance uses an 18% gray card as a neutral reference.)
A digital camera's auto white balance is often more effective when the photo contains at least one white or bright colorless element. Of course, do not try to change your composition to include a colorless object, but just be aware that its absence may cause problems with the auto white balance. Without the white boat in the image below, the camera's auto white balance mistakenly created an image with a slightly warmer color temperature.

Multiple illuminants with different color temperatures can further complicate performing a white balance. Some lighting situations may not even have a truly "correct" white balance, and will depend upon where color accuracy is most important.
White Balance Example: Mixed Lighting
Reference:MoonStone
Under mixed lighting, auto white balance usually calculates an average color temperature for the entire scene, and then uses this as the white balance. This approach is usually acceptable, however auto white balance tends to exaggerate the difference in color temperature for each light source, as compared with what we perceive with our eyes.
Exaggerated differences in color temperature are often most apparent with mixed indoor and natural lighting. Critical images may even require a different white balance for each lighting region. On the other hand, some may prefer to leave the color temperatures as is.
Note how the building to the left is quite warm, whereas the sky is somewhat cool. This is because the white balance was set based on the moonlight — bringing out the warm color temperature of the artificial lighting below. White balancing based on the natural light often yields a more realistic photograph. Choose "stone" as the white balance reference and see how the sky becomes unrealistically blue.

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Understanding Digital Camera Histograms: Luminosity and Color

This section is designed to help you develop a better understanding of how luminosity and color both vary within an image, and how this translates into the relevant histogram. Although RGB histograms are the most commonly used histogram, other types are more useful for specific purposes

The image below is shown alongside several of the other histogram types which you are likely to encounter. Move your mouse over the labels at the bottom to toggle which type of color histogram is displayed. When you change to one of the color histograms a different image will be shown. This new image is a grayscale representation of how that color's intensity is distributed throughout the image. Pay particular attention to how each color changes the brightness distribution within the image, and how the colors within each region influence this brightness.



Choose:REDGREENBLUEALL

Luminance* histograms are more accurate than RGB histograms at describing the perceived brightness distribution or "luminosity" within an image. Luminosity takes into account the fact that the human eye is more sensitive to green light than red or blue light. View the above example again for each color and you will see that the green intensity levels within the image are most representative of the brightness distribution for the full color image. This also reflected by the fact that the luminance histogram also matches the green histogram more than any other color. Luminosity correctly predicts that the following stepped gradient gradually increases in lightness, whereas a simple addition of each RGB value would give the same intensity at each rectangle.

darkestlightest

How is a luminance histogram produced? First, each pixel is converted so that it represents a luminosity based on a weighted average of the three colors at that pixel. This weighting assumes that green represents 59% of the perceived luminosity, while the red and blue channels account for just 30% and 11%, respectively. Move your mouse over "convert to luminosity" below the example image to see what this calculation looks like when performed for for each pixel. Once all pixels have been converted into luminosity, a luminance histogram is produced by counting how many pixels are at each brightness — identical to how a histogram is produced for a single color.

*Technical Note: Strictly speaking, these should really be called "luminosity histograms." Unfortunately, the terms "luminance" and "luminosity" are often used interchangeably, including by Photoshop, even though each describes a different aspect of light intensity. Luminance refers to the absolute amount of light emitted by an object per unit area, whereas luminosity refers to the perceived brightness of that object by a human observer.

An important difference to take away from the above calculation is that while luminance histograms keep track of the location of each color pixel, RGB histograms discard this information. An RGB histogram produces three independent histograms and then adds them together, irrespective of whether or not each color came from the same pixel. To illustrate this point we will use an image which the two types of histograms interpret quite differently.



The above image contains many patches of pure color. At the interior of each color patch the intensity reaches a maximum of 255, so all patches have significant color clipping and only in that color. Even though this image contains no pure white pixels, the RGB histogram shows strong clipping—so much that if this were a photograph the image would appear significantly overexposed. This is because the RGB histogram does not take into account the fact that all three colors never clip in the same place.

The luminance histogram tells an entirely different story by showing no pixels anywhere near full brightness. It also shows three distinct peaks—one for each color that has become significantly clipped. Since this image contains primarily blue, then red, then least of all green, the relative heights clearly show which color belongs where. Also note that the relative horizontal position of each peak is in accordance with the percentages used in the weighted average for calculating luminance: 59%, 30%, and 11%.

So which one is better? If we cared about color clipping, then the RGB histogram clearly warns us while the luminance histogram provides no red flags. On the other hand, the luminance histogram accurately tells us that no pixel is anywhere near full black or white. Each has its own use and should be used as a collective tool. Since most digital cameras show only a RGB histogram, just be aware of its shortcomings. As a rule of thumb, the more intense and pure the colors are in your image, the more a luminance and RGB histogram will differ. Pay careful attention when your subject contains strong shades of blue since you will rarely be able to see blue channel clipping with luminance histograms.

Whereas RGB and luminance histograms use all three color channels, a color histogram describes the brightness distribution for any of these colors individually. This can be more helpful when trying to assess whether or not individual colors have been clipped.

View Channel:REDGREENBLUEALLLUMINOSITY


View Histogram:RGBLUMINOSITY

The petals of the red flowers caught direct sunlight, so their red color became clipped, even though the rest of the image remained within the histogram. Regions where individual color channels are clipped lose all texture caused by that particular color. However, these clipped regions may still retain some luminance texture if the other two colors have not also been clipped. Individual color clipping is often not as objectionable as when all three colors clip, although this all depends upon what you wish to convey.

RGB histograms can show if an individual color channel clips, however they do not tell you if this is due to an individual color or all three. Color histograms amplify this effect and clearly show the type of clipping. Move your mouse over the labels above to compare the luminance and RGB histograms, to view the image in terms of only a single color channel, and to view the image luminance. Notice how the intensity distribution for each color channel varies drastically in regions of nearly pure color. The strength and purity of colors within this image cause the RGB and luminance histograms to differ significantly.

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