Color white balance algorithm
The white balance algorithm was to find the neutral color points of different brightness in the image (these points should theoretically satisfy R = G = B, but they may not be equal due to color cast), and then map them into equal values to obtain the action matrix of balanced color, and then carry out color balance processing on the whole image. Calculating the brightness of the original image with color deviation, the point close to the maximum brightness value is defined as a white point, calculating the average value of the R, G, and B color components and the brightness average value of all the white point set, and then calculating the adjustment parameters of color balancing according to a specific formula. Finally, the color balance adjustment is carried out on the R, G, and B color components of the entire image. There was also a multi-mode white balance algorithm that was based on the traditional white balance algorithm in the field of cameras. It added an AI white balance algorithm and an auxiliary color temperature sensor to integrate a variety of information to achieve a better white balance effect. This algorithm could accurately identify the true color temperature of the current light in the green or blue screen background scene, thus perfectly restoring the true color of the host and the product. It could achieve the true and natural color restoration of objects in all color temperature environments, and the skin color effect of people was excellent. Increases the authenticity and stability of the white balance, the ability to express colors, and the ability to restore the atmosphere. Read more exciting novels for free
White balance algorithm classification
White balance algorithms were commonly categorized into Gray World Assimilation, Perfect Reflection Assimilation, and dynamic threshold method. Among them, the average, perfect reflection, gray world, color correction, dynamic threshold, and so on can also be used as different white balance algorithm strategy modes. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
High-pass white balance algorithm
The white balance algorithm was mainly based on the grey world assumption, and most white balance algorithms were based on this assumption. The process of the algorithm was as follows: first, a reference point (reference lighting environment) was established, then the statistics of the color temperature were collected, and then the statistics were analyzed to identify the current color temperature. Finally, according to the identified color temperature environment, the corresponding RGH was supplemented to achieve white balance. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
White balance inconsistent with the environment
White balance was a setting used to ensure that white objects appeared white during shooting. Under normal circumstances, white balance should be adjusted according to the surrounding light to restore color. However, if the white balance setting was not consistent with the environment, it would produce a special effect. For example, if the white balance is set to a higher color temperature (such as a high color temperature value corresponding to more blue light) in an environment with a lower color temperature (red and yellow light), the overall image will be colder, which is inconsistent with the actual warm color environment. On the contrary, if the white balance is set to a lower color temperature in an environment with a high color temperature (blue light), the image will be warmer. This white balance setting that was inconsistent with the environment could be used in creative photography to create a unique atmosphere, express specific emotions, or achieve artistic visual effects by deliberately breaking the conventional white balance setting. In some scenes that needed to highlight subjective emotions or special visual effects, the photographer would adjust the white balance according to his own creative needs, so that it did not match the actual color temperature of the environment, rather than simply pursuing the accurate restoration of the color of the environment. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
White balance Detection Tools
The following are some white balance Detection Tools: - In PS, you could enter the Camera Raw through the filter " Camera Raw " filter. The white balance tool in the basic panel could be used to correct the color bias of the photo. The white balance tool was a slanted straw icon that could absorb the reference object in the picture. The ACR would automatically determine and adjust the white balance parameters. - The gray card in the Anwenk Gray Card Set was a basic white balancing tool. The set also included pure white and real black cards. - VidPro WD- 24 white balance disc, with an 18% gray exposure target on one side and a white surface on the back. It was foldable and could be placed in a suitcase when not in use. - ExpoDisc 2.0 white balance filter was the industry standard for white balance filter. It could capture the incoming light reading and set a custom white balance accordingly. It was available in 77mm and 82mm thread size. - Some cameras had a white balance evaluation function, such as a LCD camera, which could detect the appropriate white balance setting by placing your finger in the center of the screen and constantly changing the camera's white balance according to the environment. - In the premiere, if there was a white object in the photo, you could use the " straw " tool behind the white balance to click on the white object for automatic white balance correction. You could also manually adjust the color temperature and color, but you needed color knowledge. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
White balance adjustment of the surveillance camera
White balance refers to the restoration of white objects by the camera, which can correct the color difference between indoor and outdoor environments. For color cameras, white balance could be divided into manual and automatic. If the camera was found to have a serious color cast, it might be a problem with the white balance. White balance adjustment can be performed in the Fluorites Studio. The setting path is: (local area network) Fluorites Studio-Equipment Management-Advanced configuration-Front-end parameters-White balance. The white balance effect can be changed by adjusting the white balance gain. The greater the gain, the more obvious the effect. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
This white color bleaches English
The original white could be translated as raw white or off white, while the white could be translated as bleed or white.
vegetable recognition algorithm
The most accurate algorithm
Here are some betting tips: 1. ** Cold Tracking **: Cold refers to the phenomenon of missing numbers in the process of individual or group numbers gradually increasing in the lottery period. It is another form of hidden numbers after the dominant number. 2. ** N-Code Duplicated Pack Number **: Duplicated N-Code Double-Throw was widely used in the lottery market. The advantage of this method was that it was easy to bet after killing several numbers accurately, and the probability of winning was high. 3. Single Stare Double Shot: When the trend is extremely symmetrical and the inspiration is strong, it is even more wonderful to hit the bullseye with a single Stare Double Shot. 4. [Pyramid Betting Method (applicable when multiple cold states appear at the same time): List all the popular numbers, then rank them according to the comprehensive index, and then carry out multiple bets of different amounts. Finally, select a multi-bet single-number multiple bet.] It should be noted that although there are these betting techniques, there is no guarantee that you will win the game with absolute accuracy. Buying lottery tickets should be rational and calm to avoid over-investment. The novel "Watching the Moon on Fish Island" is equally exciting. Everyone is welcome to click and read it!
Distance measurement algorithm
The following is an algorithm for measuring distance with fingers (using the thumb as an example): Raise your right arm horizontally, make a fist with your right hand, and raise your thumb. Using his right eye (left eye closed), he overlapped the left side of his thumb with the target in a straight line. If the right arm and thumb were still, he would close his right eye and observe the left side of the thumb with his left eye. He would find that the boundary was a distance away from the right side of the target. He estimated the distance and multiplied it by 10. The result was the approximate distance to the target. This method required a certain amount of experience, and there were some objective things that could provide some reference. For example, the size of the house and the distance between the houses were generally about 10 meters, or the distance between the utility poles was 50 meters, the town utility poles were 100 meters, and the high-voltage power was 200 meters. He still needed to practice more to be able to use it skillfully, and the measurement error would be smaller. The other way was to place your thumb in front of you (with your arm fully extended) and close one eye. Hover your thumb over an object of known size (such as a car). In the case of not moving the thumb, close the open eye and open the other eye to estimate the distance that the thumb has "moved" relative to the object you are looking at. For example, the length of a car is known to be about 4.5 meters. If the thumb moves half of the length of the car (about 2.25 meters), multiply the result by 10 to obtain the approximate distance between the car and you (in this case, about 22.5 meters). <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
Meitu photography algorithm
Meitu Soo had many achievements in photography algorithms. For example, it has been connected to the underlying algorithm of Huawei's system-level camera. Through this cooperation, the functions of Meitu's camera have been optimized and strengthened to provide a better photo experience. The Xiaomi CC9 Meitu customized version of the 32 million selfie camera uses Meitu's AI low-light algorithm to take clear photos even in low-light environments. Meitu Xiu Xiu's own strength and status in the retouching world had been recognized. The upgrade of its camera function had been pursuing a natural texture and innovative gameplay. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>