Perform histogram equalization for the following histogram.
| Gray level | 0 | 1/7 | 2/7 | 3/7 | 4/7 | 5/7 | 6/7 | 7/7 |
| Number of occurrences | 400 | 700 | 800 | 900 | 500 | 400 | 196 | 200 |
The given histogram has eight discrete gray levels from 0 to 7/7 with corresponding numbers of occurrences. The total number of pixels is obtained by summing all occurrences, which gives 4096 pixels. The probability of occurrence for each gray level is found by dividing the number of occurrences at that level by 4096. Thus, _______ ________ _________ __________ ____ ___ ______ ____ _____ ______.
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Explain in detail the adaptive mean and median filters.
b) Obtain mean and variance of the following noise pdfs:
i)
ii)
iii)
Explain the Hough transform for edge linking with suitable example.
Apply the perception algorithm to the following pattern classes:,
.
Let and
Also, Sketch the decision surface.
In image restoration, how are the noise parameters estimated?
b) Assume that the noise is estimated as exponential, with mean Variance
. How will you estimate the parameter 'a' of pdf of exponential Noise?
) Given an image f(x,y) with Fourier transform F(u,v) obtain the fourier transform of .
c) Given that the 2-D Fourier Tranform is real and even, obtain the constraints on the image characteristics.
Given that
where f and g are real images and h is a spatial filter.
Obtain G(u,v), in terms of F(u,v), and H(u,v), the 2–D Fourier transform of g(x,y).
Perform histogram equalization for the following histogram.
| Gray level | 0 | 1/7 | 2/7 | 3/7 | 4/7 | 5/7 | 6/7 | 7/7 |
| Number of occurrences | 400 | 700 | 800 | 900 | 500 | 400 | 196 | 200 |