IGNOU MMTE 3 SOLVED ASSIGNMENT

Ques 1.

Show that Sobel masks can be implemented by one pass of differencing mask of the form [−1 0 1](or its vertical counterpart) followed by a smoothing mask of the form [1 2 1] (or its vertical counterpart).

Ques 2.

 Explain the Hough transform for edge linking with suitable example.

Ques 3.

) Propose a gray level slicing algorithm capable of producing the 4-bit plane of an 8-bit monochrome image.

Ques 4.

Explain the functioning of an adaptive, local noise reduction filter.

Ques 5.

Given an image with uniform histogram. Explain the effect of applying following compression techniques:
i) Huffman,

ii) Golomb,

iii) LZW,

iv) Prediction coding, and

v) Optimal Quantization

Ques 6.

Explain in detail the adaptive mean and median filters.
b) Obtain mean and variance of the following noise pdfs:
i) 
ii) 
iii) 

Ques 7.

 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?

Ques 8.

Suppose a low-pass spatial filter is formed by averaging the four immediate neighbours of a point (x, y) but excluding the point itself. Find the equivalent filter H(u,v) in the frequency domain.

Ques 9.

 Apply the perception algorithm to the following pattern classes:
,

.
Let  and 
Also, Sketch the decision surface.

Ques 10.

 Perform histogram equalization for the following histogram.

Ques 11.

 A bullet is 2.5 cm long, 1cm wide and its range of speed is . The bullet is flight is captured by a camera that exposes the scene for k sec and the bullet occupies 10% of the horizontal resolution of  frames. Find
i) Automatic segmentation of the bullet.

ii) Automatic determination of speed of the bullet.

Ques 12.

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).

Ques 13.

 Describe the homographic filtering. Explain why the filtering scheme is effective for the applications it is used.

Ques 14.

 Filter the given  gray level image with

i)  mean filter using zero padding

ii) 3×3 weighted mean filter using zero padding with mask

iii) 3×3 median filter processing only such pixels that have all the needed neighbours.
iv) Laplacian filter with the given mask and reflecting the border pixels.

Ques 15.

Define the following with suitable examples
i) Unsharp marking

ii) High boost filtering

iii) High frequency filterin

Ques 16.

) 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.

Ques 17.

Show that Sobel masks can be implemented by one pass of differencing mask of the form [−1 0 1](or its vertical counterpart) followed by a smoothing mask of the form [1 2 1] (or its vertical counterpart).

Ques 18.

 Explain the Hough transform for edge linking with suitable example.

Ques 19.

) Propose a gray level slicing algorithm capable of producing the 4-bit plane of an 8-bit monochrome image.

Ques 20.

Explain the functioning of an adaptive, local noise reduction filter.

Ques 21.

Given an image with uniform histogram. Explain the effect of applying following compression techniques:
i) Huffman,

ii) Golomb,

iii) LZW,

iv) Prediction coding, and

v) Optimal Quantization

Ques 22.

Explain in detail the adaptive mean and median filters.
b) Obtain mean and variance of the following noise pdfs:
i) 
ii) 
iii) 

Ques 23.

 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?

Ques 24.

Suppose a low-pass spatial filter is formed by averaging the four immediate neighbours of a point (x, y) but excluding the point itself. Find the equivalent filter H(u,v) in the frequency domain.

Ques 25.

 Apply the perception algorithm to the following pattern classes:
,

.
Let  and 
Also, Sketch the decision surface.

Ques 26.

 Perform histogram equalization for the following histogram.

Ques 27.

 A bullet is 2.5 cm long, 1cm wide and its range of speed is . The bullet is flight is captured by a camera that exposes the scene for k sec and the bullet occupies 10% of the horizontal resolution of  frames. Find
i) Automatic segmentation of the bullet.

ii) Automatic determination of speed of the bullet.

Ques 28.

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).

Ques 29.

 Describe the homographic filtering. Explain why the filtering scheme is effective for the applications it is used.

Ques 30.

 Filter the given  gray level image with

i)  mean filter using zero padding

ii) 3×3 weighted mean filter using zero padding with mask

iii) 3×3 median filter processing only such pixels that have all the needed neighbours.
iv) Laplacian filter with the given mask and reflecting the border pixels.

Ques 31.

Define the following with suitable examples
i) Unsharp marking

ii) High boost filtering

iii) High frequency filterin

Ques 32.

) 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.

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