Deep Learning/CNN

(prerequisite-RoIs) Interpolation, Linear Interpolation, Bilinear Interpolation, ZOH(Zero-order Hold Interpolation)

Naranjito 2023. 7. 4. 10:53
  • Interpolation

 

It is estimating the value between two known values.

 

  • Extrapolation

It is estimating a value at an external location outside the range.

 

  • Linear Interpolation

 

A method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.


  • Formula

 

d1 : The distance from x to x1.

d2 : The distance from x to x2.



If the distance ratio is normalized,

α = d1/(d1+d2), β = d2/(d1+d2)

 

so that the sum is 1,

 

(α + β = 1)

 

 it can be simplified as follow.

 

https://darkpgmr.tistory.com/117

 

  • Bilinear Interpolation

A method that uses the distance weighted average of the four nearest pixel values to estimate a new pixel value.


 

1) Find the x-axis and y-axis distance ratios between the four points 6, 9, 10, 12 and (5,5).

 

2) The values of P and Q are obtained by applying linear interpolation to the x-axis.

 

3) Apply linear interpolation to the y-axis to find the value of (5,5) from P and Q.

 

https://blog.naver.com/aorigin/220947541918

 

  • ZOH(Zero-order Hold Interpolation)

Real systems do not produce sample data and nor is the input to a real system a series of impulses. So what we need to do next is consider the interconnection between a computer world with sampled signals and a real world with continuous systems.

 

It is one thing to give a pictorial representation of how to connect the discrete and continuous worlds.

 

'Hold' means the most recently received value.

If a computer outputs a series of impulses as below, ZOH converts output into a staircase.

For instance, we keep the most recent value until we receive a new value, as this approximates to repeated small steps. So your reconstructed signal is a bit like a staircase. 

 

From a physical arrangement point of view, a zero hold is implemented as in the figure below.




U(z) : the discrete signal
U(s) : a continuous time signal, it gives us the staircase or pseudo continuous 
G(s) : the real system
Y(s) : continuous output

 

- Combine all the elements together leads to an information flow as below.

It computing component handles the discrete information.

 

- Mathematical formulation




* : the sampled signal
A/s : constants
e-nT : action being active from a given point by delays
T : a rectangular wave
y*(0) : being the value
H(s) : it gives us the staircase for a single impulse

subtract (1/s) from (e-sT/s) as below. 



Then, the result is this, naught and T.

 

reference : https://www.youtube.com/watch?v=vnhAG5NiYqM&t=304s

'Deep Learning > CNN' 카테고리의 다른 글

LeNet-5  (0) 2024.01.04
ResNet  (0) 2024.01.03
AlexNet  (0) 2024.01.03
VGG16  (0) 2023.12.14
CNN  (0) 2023.01.20