Automatic white balance seems to balance out the color of the lighting so that colors can be properly contrasted, as opposed to everything looking a different shade of the light. What does this formula define as a "white object" My guess is some threshold for the RGB values, so i wonder how this threshold will effect the transformation. As the threshold for being white goes down, the more color of the lighting will be present in the transformed image.
saif-m17
I'm a bit confused on what exactly dividing by the input of a white object does exactly for our color values. I sort of have a sense for what white balancing does from the examples in the previous few slides, but I was wondering if there was a way to specifically explain what we're accomplishing in a more technical sense.
saif-m17
Additionally, we noted in lecture that there was a lot of work done to figure out how to actually find values for Rw', Gw', and Bw'. I'm curious what some of these methods look like.
dhruvchowdhary
@saif-m17 Dividing by the white object's color adjusts all colors in the image to make them look like they're under white light. Think of it as a filter that corrects the color tint from the lighting, making the colors true to life. It's like setting a baseline; once the camera knows what white looks like under a certain light, it can adjust all the other colors correctly.
dhruvchowdhary
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Boomaa23
Why do we represent this in matrix form if our only values are on the diagonal? (i.e. we have the result that each color value is divided by the white object reading). Surely computing all these zeroes would be unnecessary computation?
Boomaa23
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wilrothman
This will likely be a dumb thought, but I noticed that color is often represented as an R3 RGB vector in this class. I wonder if there is something fundamental about color being 3-dimensional. Like, I know that our world exists in 3D space, and that color is different than space. On the other hand, I remember being taught in elementary school that there are three primary colors: red, green and blue. In high school, I was taught that there are three cones-- those that sense red, green, and blue light respectively. Finally, in college, I was taught about RGB pixel values. Notice that all of these representations, though different, are 3 dimensional. I am also aware that color is a continuous spectrum representing photon wavelengths. I wonder if my 3-dimensional understanding of color is rooted in biology, or if there is a more fundamental reason for this.
S-Muddana
It is also interesting to see that you can tie automatic white balancing with machine learning by training a model on many images so it can learn what a correct color balance is like so it can adjust the balance of new colors.
agao25
My understanding of this slide is that we can use this matrix operation to make our captured or rendered images appear as if under white light. And this setting can be adjusted to other types of "white." I'm curious what kind of research and work is done to create the Rw', Gw', and Bw' values since it seems like we are just going to always assume white balance has already been done in this class.
sebzhao
This is really cool to see how automatic white balance works because we take it for granted in cameras. It makes sense that we are effectively normalizing.
jananisriram
What does automatic white balance look like on an image visually?
Automatic white balance seems to balance out the color of the lighting so that colors can be properly contrasted, as opposed to everything looking a different shade of the light. What does this formula define as a "white object" My guess is some threshold for the RGB values, so i wonder how this threshold will effect the transformation. As the threshold for being white goes down, the more color of the lighting will be present in the transformed image.
I'm a bit confused on what exactly dividing by the input of a white object does exactly for our color values. I sort of have a sense for what white balancing does from the examples in the previous few slides, but I was wondering if there was a way to specifically explain what we're accomplishing in a more technical sense.
Additionally, we noted in lecture that there was a lot of work done to figure out how to actually find values for Rw', Gw', and Bw'. I'm curious what some of these methods look like.
@saif-m17 Dividing by the white object's color adjusts all colors in the image to make them look like they're under white light. Think of it as a filter that corrects the color tint from the lighting, making the colors true to life. It's like setting a baseline; once the camera knows what white looks like under a certain light, it can adjust all the other colors correctly.
[deleted]
Why do we represent this in matrix form if our only values are on the diagonal? (i.e. we have the result that each color value is divided by the white object reading). Surely computing all these zeroes would be unnecessary computation?
[deleted]
This will likely be a dumb thought, but I noticed that color is often represented as an R3 RGB vector in this class. I wonder if there is something fundamental about color being 3-dimensional. Like, I know that our world exists in 3D space, and that color is different than space. On the other hand, I remember being taught in elementary school that there are three primary colors: red, green and blue. In high school, I was taught that there are three cones-- those that sense red, green, and blue light respectively. Finally, in college, I was taught about RGB pixel values. Notice that all of these representations, though different, are 3 dimensional. I am also aware that color is a continuous spectrum representing photon wavelengths. I wonder if my 3-dimensional understanding of color is rooted in biology, or if there is a more fundamental reason for this.
It is also interesting to see that you can tie automatic white balancing with machine learning by training a model on many images so it can learn what a correct color balance is like so it can adjust the balance of new colors.
My understanding of this slide is that we can use this matrix operation to make our captured or rendered images appear as if under white light. And this setting can be adjusted to other types of "white." I'm curious what kind of research and work is done to create the Rw', Gw', and Bw' values since it seems like we are just going to always assume white balance has already been done in this class.
This is really cool to see how automatic white balance works because we take it for granted in cameras. It makes sense that we are effectively normalizing.
What does automatic white balance look like on an image visually?