Solving Image Problems with Orthonormal Systems - M. Next

In summary, M. nextThe net charge of the image charges should be -q shouldn't it? If you add the two charges that you made at (-1,1) and (1,-1), those would add up to be -2q. Thus, you need 1 more +q charge at (-1,-1).Who said that the net charge must be -q?And another thing: if y-axis and x-axis are not perpendicular but they have an acute angle between them (say 60) and q is placed at (4,1) as in the old orthonormal system (am only placing coordinates to clear up the image). What will happen? What should we do then? How do we treat this case
  • #1
M. next
382
0
Hello :)
I would be very grateful if I get through with this bugging idea.
When I started reading about it, the first example was: A point charge in front of a finite plane conductor.
And I understood after some mathematical and geometrical procedure how it is okay to replace the finite plane with a charge of opposite sign.
Then I bumped into another problem:
Let us consider orthonormal system and point charge +q is placed on point (1,1) - it's image being in symmetry with respect to y-axis will be at (-1,1) and it's image abeing in symmetry with respect to x-axis will be at (1,-1). Until here I was okay, till I realized that another point charge was placed at (-1,-1). Why?
What's the whole point?

Thanks in advance.

M. next
 
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  • #2
The net charge of the image charges should be -q shouldn't it? If you add the two charges that you made at (-1,1) and (1,-1), those would add up to be -2q. Thus, you need 1 more +q charge at (-1,-1).
 
  • #3
Who said that the net charge must be -q?
And another thing: if y-axis and x-axis are not perpendicular but they have an acute angle between them (say 60) and q is placed at (4,1) as in the old orthonormal system (am only placing coordinates to clear up the image). What will happen? What should we do then? How do we treat this case?
 
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  • #4
If your angle is theta then the number of image charges you would need would be 360/theta -1. This number must be an integer so only angles that divide 360 are allowed for this method to work.
 
  • #5
nucl34rgg, I appreciate it a lot. But can you please tell me how do you know these equations? Because I read the whole course and didn't come across these equations.
And then you told me the nb of images but you didn't tell me where to locate them?
 
  • #6
Ok this picture is embarrassingly bad, but it will serve the purpose. Pretend each angle between the lines is 60 degrees, and pretend that the figure is radially symmetric. All you have to do is reflect around and go around in a circle. Treat the one +q in the top right as real and the rest are image charges. Then the lines along the boundary for that +q have V=0.
 

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  • #7
Thanks for your time, I really appreciate it. But what do you mean by "reflect around and go around in a circle"
You must be fed up by now, but I have to understand this by tomorrow.
Thanks again
 
  • #8
start with the positive q charge on the top right. reflect over the v=0 boundary line. add a -q charge there and reflect over the next line...add a +q charge there, etc
 

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  • #9
Oh now i get it! Finally!
Thank you loads nucl34rgg. I really appreciate your time, and patience. :)
 
  • #10
Glad to help! :)
 

1. What is an orthonormal system?

An orthonormal system is a set of vectors that are both orthogonal (perpendicular) and normalized (have a magnitude of 1). This means that the vectors are at right angles to each other and have a length of 1, making them useful for representing images and solving image problems.

2. How are orthonormal systems used in solving image problems?

Orthonormal systems are used in solving image problems by providing a basis for representing images. By decomposing an image into its constituent orthonormal vectors, we can manipulate and analyze the image in a more efficient way, allowing us to solve various image problems such as image reconstruction, compression, and denoising.

3. What are the advantages of using orthonormal systems in image processing?

There are several advantages to using orthonormal systems in image processing. One major advantage is that they allow for efficient representation of images, which can save storage space and reduce computational complexity. Additionally, orthonormal systems can preserve important information in an image while reducing noise and artifacts, making them useful for image denoising and compression.

4. How do you choose an appropriate orthonormal system for a specific image problem?

Choosing an appropriate orthonormal system for a specific image problem depends on the characteristics of the image and the goals of the problem. Some commonly used orthonormal systems include the discrete cosine transform (DCT), discrete wavelet transform (DWT), and discrete Fourier transform (DFT). Each of these systems has its own strengths and weaknesses, so it is important to evaluate which system would be most suitable for the specific image problem.

5. Can orthonormal systems be used in other fields besides image processing?

Yes, orthonormal systems have applications in various fields besides image processing. They are commonly used in signal processing, data compression, and quantum mechanics, among others. Orthonormal systems provide a versatile and efficient way to represent and analyze data, making them valuable tools in many scientific and engineering fields.

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