Describe the Kernel and Image

In summary: If the OP does not know, he has to ask. If he does not ask, he cannot expect a sensible answer. In this case, there is nothing to answer. We cannot tell what ##\mathbb{R}^\times## is in this context (or if it is not in this context). So I think this thread is pointless.
  • #1
RJLiberator
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Homework Statement


φ is a homomorphism of groups.
φ: ℝ^x -> ℝ^x, where φ(α) = α^4, for all α ∈ ℝ^x. Note that ℝ^x is a group under multiplication.

Describe ker(φ) and Im(φ).

Homework Equations

The Attempt at a Solution



This is another one of those problems that has me scratching my head due to the way it is written.

What does ℝ^x mean? A real number exponentiated?
If all it does is take α and map it to α^4, then the kernal is just going to be the same as it would be without the mapping?
The image is just exponentiated 4 times.

er?
 
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  • #2
RJLiberator said:

Homework Statement


φ is a homomorphism of groups.
φ: ℝ^x -> ℝ^x, where φ(α) = α^4, for all α ∈ ℝ^x. Note that ℝ^x is a group under multiplication.

Describe ker(φ) and Im(φ).

Homework Equations

The Attempt at a Solution



This is another one of those problems that has me scratching my head due to the way it is written.

What does ℝ^x mean?

Good question. How should we know? It's your problem so you tell us. Look it up in your book or course notes and tell us. Or maybe when you find out what it is, you will know how to do the problem.
 
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  • #3
For any two sets ##A## and ##B##, the symbol ##A^B## denotes the set of all functions with ##B## as domain and ##A## as range. If you think about this, starting with things like ##A=\{0,9\}## and ##B=\{1,2,3,4\}## (the set of combinations of a lock with four barrels each marked with 0...9 and then moving on to cases like ##A=\{0,1\}## and ##B=\mathbb{N}## (the set of all binary representations of real numbers in ##[0,1]## ) you'll see why this definition works.
 
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  • #4
andrewkirk said:
For any two sets ##A## and ##B##, the symbol ##A^B## denotes the set of all functions with ##B## as domain and ##A## as range. If you think about this, starting with things like ##A=\{0,9\}## and ##B=\{1,2,3,4\}## (the set of combinations of a lock with four barrels each marked with 0...9 and then moving on to cases like ##A=\{0,1\}## and ##B=\mathbb{N}## (the set of all binary representations of real numbers in ##[0,1]## ) you'll see why this definition works.

Are you addressing that to the OP or to me? If to me, I know that. How does that explain what set x is in ℝ^x if that's what it means?
 
  • #5
LCKurtz said:
Are you addressing that to the OP or to me?
The OP. Sorry if that wasn't clear. My impression is that the default assumption is that a post in an internet forum that doesn't quote or name a specific poster is addressing the OP. But the trouble is that nobody ever spells out the etiquette for this, so different people may be operating under different default assumptions.

BTW I don't think it matters what x is. It does not need to be specified in order to answer the two questions asked.
 
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  • #6
@RJLiberator
If ##\alpha## is a real-valued function with domain ##x## then the only workable interpretation I can think of ##\alpha^4## is that ##\alpha^4:x\to\mathbb{R}## such that, for ##y\in x,\ (\alpha^4)(y)=(\alpha(y))^4##.

The identity element of ##\mathbb{R}^x## will be the function that maps all of ##x## to a single number (what number?). One can then work out what the kernel of ##\phi## must be.

To work out what Im##\phi## is, think about what sorts of functions from ##x## to ##\mathbb{R}## cannot give the same results that are given by a function of the form ##\alpha^4##.

I wonder why they chose ##\phi=\alpha^4## though, because I think the answers to the two questions would be the same if we had ##\phi=\alpha^2##. If the range were ##\mathbb{C}## rather than ##\mathbb{R}## then the answers for ##\alpha^4## and ##\alpha^2## would differ.
 
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  • #7
Neither of that is what ##\mathbb{R}^\times## means in this context. I know what it means, but I consider it the responsability of the OP to tell us what his notation mean.
 

1. What is the Kernel of an image?

The Kernel of an image is a small matrix of numbers that is used in image processing to perform various operations such as blurring, sharpening, and edge detection. It is typically a square matrix with odd dimensions, such as 3x3 or 5x5, and contains numerical values that are multiplied with the corresponding pixels in the image to produce a new output image.

2. How does the Kernel affect an image?

The Kernel affects an image by altering the values of the pixels in the image based on the values in the Kernel matrix. For example, a Kernel with high values in the center and low values at the edges can be used for blurring, as it will blur the image by averaging the pixel values. Different Kernel matrices can produce different effects on the image, such as sharpening, smoothing, or highlighting edges.

3. What is the difference between the Kernel and the Image?

The Kernel is a small matrix of numerical values that is used to perform operations on an image, whereas the Image is a two-dimensional array of pixels that make up the visual representation. The Kernel is a tool used to manipulate the image, while the Image is the actual data being manipulated.

4. Can the Kernel be customized for different types of images?

Yes, the Kernel can be customized to produce different effects on different types of images. For example, a Kernel that works well for blurring a photograph may not work as well for blurring a text document. The values in the Kernel can be adjusted to achieve the desired effect for a specific type of image.

5. Is the Kernel the same for all image processing techniques?

No, the Kernel can vary depending on the technique being used for image processing. Different techniques may require different Kernel matrices to achieve the desired effect. Additionally, the size of the Kernel may also vary depending on the technique and the desired outcome.

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