Image Processing - Unit Wise Questions

1. What is digital image and digital image processing? Explain.

1. Differentiate between digital image representation and digital image processing.

1. Explain the digital image processing with example.[6]

2. Explain the fast fourier with example.

2. Explain the adjacency and path of image pixels. Calculate the 8-adjacent and m-adjacent path from (1,3) to(3,2) for following image on V{0,1}.

0  2  1

0  2  0

0  0  1

2. Define the fourier transform. Explain the Hadamard transform with example.[2+4]

2. Explain the Haar Transform with suitable example.(6)

1. Explain how you can convert an analog image into a digital image. How many images of size 2400X1600 with 256 gray levels can be stored in a 1024 MB storage space?(3 + 3)

3. Explain the power law transformations. Write the algorithm for the implementation of median filter in spatial domain.(2+4)

3. What do you mean by image enhancement? Explain the Histogram Modeling with example.

3. What is Histogram Modeling? Compute the histogram equalization of the given data.

3. What do you mean by visual perception? What are the elements of visual perception? Explain.[3+3]

2. Define Discrete Cosine Transform(DCT). Differentiate between Discrete Fourier Transform (DFT) and Fast Fourier Transform(FFR).

1. Differentiate between Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT). Explain the FFT algorithm for one-dimensional case. [3+7=10]

1. Explain "power law transformation" techniques for the purpose of image enhancement. Explain the mean filter along with suitable algorithm for its implementation(4+6)

4. What do you mean by image enhancement? Explain the operation of Power law (Gamma) transformation with example.

4. What do you mean by low pass filtering in digital image processing? Explain it with suitable example. Show how can you convert low pass filter to high pass filter with suitable block diagram.(6)

2. Given the following frequency table obtained from the histogram of a 16 X 16, 8 level image.

M    0     1      2     3    4    5      6     7

N_M 15    6    70   16   31   35   32   51

Where M = gray level and NM = pixels having Mth gray level.

Construct Huffmann code for each gray level. Calculate the compression ratio and the relative data redundancy assuming if 3-bit code is used to code the gray level instead of Huffmann code. [7+3 =10]

4. What do you mean by image coding? Explain the process of predictive coding.

4. Explain the template matching with algorithm and example.[6]

3. Explain the term 'Log and inverse log transformation' techniques for the purpose of image enhancement. Explain the average spatial filter along with suitable algorithm for its implementation.(2+4)

5. Explain the features extraction with appropriate example . Why feature extraction is important in image processions? Explain.

2. What is Fourier Transform and how can you apply it in the digital image processing? Explain the different properties of the Fourier Transform.(4+6)

3. What is an edge detection filter? Differentiate between the first derivative and second derivative filter? Derive the filter mask for laplacian filter and write the algorithm for its implementation. [1+2+7=10]

5. What do you mean by Lossy Predictive Coding? Explain it with a suitable block diagram.(6)

5. What is image coding? Explain the Run length coding with example.[2+4]

5. Construct Huffman code for each gray level given and find the compression ratio and coding efficiency.

5. What do you mean by Lossless Predictive Coding? Explain it with a suitable block diagram.(6)

3. Explain the adaptive thresholding and regionsplit and merge techniques for image segmentation.(5+5)

6. What is boundary? Explain spatial averaging filter.

6. Explain in detail procedure for implementing Butterworth High Pass filter in Frequency domain.(6)

6. Mention the different types of character recognition and explain it.[6]

6. Explain the edge detection using templates with practical example.

6. Explain the detail procedure for implementing Butterworth Low Pass filter in Frequency domain.(6)

7. What is zooming? Explain the process of zooming by interpolation method.(6)

7.Explain the segmentation by thresholding. Explain how can you apply segmentation in line defection. 

7. What is Hough transform? How it is useful in line detection? Explain with example.

7. What is pattern recognition? Explain the applications of neural networks in pattern recognition.[2+4]

1.How many images of size 1024×768 with 256 gray levels can be stored in a 2048 MB storage space?

8. How edges are detected using the gradient operators? Explain with suitable example.

8. Explain the Hadamard transform with example.

8. Explain in detail the region split and merge techniques for image segmentation. List the problems associated with region split and merge technique.(4+2)

8. Define the edge detection. What do you mean by template matching? Explain.[2+4]

7. What is zooming? Express the process of zooming by replication method.(6)

9. Explain in detail the template matching using Correlation.(4+2)

2. Write short notes on dilation and erosion.

8. Explain in detail the region growing techniques for image segmentation. List the problems associated with region growing technique.(4+2)

9. How dilation and erosion are  applied in region filling and boundary extraction?[6]

9. What is pattern and pattern recognition? Explain the steps and application areas of pattern recognition syatem.

9. Explain the predictive and inter-frame coding with example.

10. Write short notes on:[3+3]

a) Features extraction

b) Wavelet transform

4.Discuss the various applications and problems associated with the digital image processing in brief.

10. Write short notes on:

a) Features extraction

b) Wavelet transform

3.Explain the contra-harmonic mean filters used in image restoration.

10. Differentiate between Hopfield nets and hamming nets.

10. What is a gradient filter? Explain the Sobel gradient filter in detail along with its algorithm for implementation.(1 + 5)

11. Differentiate between:

a) Sampling and quantization

b) High pass and band pass filtering

11. Write short notes on:(3 + 3)

a) Pattern Recognition and its applications

b) Neural Network

10. Derive the equation for Laplacian filter and write the algorithm for its implementation.(6)

4.Explain the Bit plane slicing technique for image enhancement.

5. Discuss the algorithm for histogram equalization.

11. Differentiate between.[3+3]

a) high pass and band pass filtering

b) line detection and edge detection

11. Write short notes on:(3+3)

a) Neural Network

b) Global thresholding

5.Explain how Hough transform is useful in line detection?

7. How will you implement Butterworth high Pass Frequency domain filter for image sharpening in the frequency domain? Describe in brief.

6.Describe in brief that how do you implement Gaussian High Pass Frequency domain filter for image smoothing in the frequency domain?

6. Explain the first derivative filter with a suitable example.

8. Explain Contra-harmonic Mean Filters used for image restoration.

7. Explain lossy predictive coding in brief.

8. Explain the region growing technique for image segmentation.

9. Describe lossless predictive coding model with a suitable block diagram.

9. Explain how can a neural network be applied in digital image processing with the help of a simple perceptron

10. Explain opening and closing morphological operations in brief.

11. Discuss the magnification of image using interpolation technique.

12. Discuss Neural Network based image recognition system with the help of a simple perception.