(SEM VIII) THEORY EXAMINATION 2017-18 DIGITAL IMAGE PROCESSING

DIGITAL IMAGE PROCESSING (NCS-801)

According to the uploaded question paper 

The Digital Image Processing examination is divided into three sections: A, B, and C. The structure of the paper progresses from fundamental image concepts to filtering and restoration techniques, and finally to advanced segmentation, transformation, and morphological operations.

Below is a detailed explanation of each section in clear descriptive format.

Section A – Basic Concepts and Fundamental Definitions (20 Marks)
 

Section A consists of ten compulsory short-answer questions, each carrying two marks. This section evaluates your understanding of core concepts in digital image processing.

The questions include definitions such as image and its range, reflectance, types of images (binary, grayscale, color), harmonic mean filter, contrast stretching, dilation and erosion, predictive coding, edge detection operators, affine transform, and thresholding.

This section checks whether you understand how images are represented digitally and how basic operations modify image characteristics. For example, reflectance refers to the amount of light reflected by an object, while dilation and erosion are morphological operations used to expand or shrink image objects. Contrast stretching enhances image visibility by expanding intensity values across the available dynamic range.

Although the answers are short, they form the theoretical base for advanced image enhancement and segmentation techniques.

Section B – Image Representation, Filtering, and Restoration (30 Marks)
 

Section B requires you to attempt any three questions, each carrying ten marks. This section focuses on image modeling, filtering techniques, restoration methods, and morphological transformations.

Topics include digital image representation, comparison between linear and non-linear spatial filtering, image restoration with block diagram, hit-miss transform, and proving that Prewitt and Sobel operators act as high-pass filters.

In this section, you are expected to provide detailed explanations and diagrams where necessary. For example, in image restoration, you should explain degradation models, noise, and filtering techniques such as inverse filtering and Wiener filtering. The block diagram typically includes original image, degradation function, noise, restoration filter, and restored image.

When comparing linear and nonlinear filters, you must explain that linear filters use weighted sums (like averaging filters), whereas nonlinear filters (like median filters) are better at removing impulse noise.

This section evaluates understanding of enhancement, filtering, and mathematical operators used in edge detection and restoration.

Section C – Advanced Segmentation, Transformation, and Morphological Operations (50 Marks)
 

Section C carries the highest weightage and requires you to attempt one part from each question. This section tests advanced concepts in segmentation, transformation, histogram processing, morphological operations, and feature detection.

Topics include region-based segmentation, intensity transformations, dynamic range analysis in contrast stretching, Hough Transform for line detection, histogram equalization, Laplacian filtering, morphological opening and closing, region extraction, image registration, edge detection algorithms, and line detection algorithms.

For example, in region-based segmentation, you must explain methods such as region growing and splitting-merging. In intensity transformation, you should describe logarithmic, power-law, and piecewise linear transformations and explain how slopes affect dynamic range.
The Hough Transform question requires mapping points from spatial domain to parameter space and detecting straight lines. For the given points (1,1), (2,2), (3,3), (4,4), you would show that they lie on the straight line y = x.

Histogram equalization requires explaining how image contrast is improved by redistributing intensity values using cumulative distribution function.

Morphological operations like opening and closing must be explained as combinations of erosion and dilation used to remove small objects or fill gaps.

This section evaluates analytical thinking, mathematical modeling, and practical implementation of image processing algorithms.

Overall Paper Structure and Preparation Strategy
 

The paper follows a structured approach:

Section A tests foundational definitions and basic operations.

Section B focuses on filtering, restoration, and spatial processing.

Section C evaluates advanced segmentation, transformation, and morphological techniques.

To perform well:

Understand image representation and intensity transformations clearly.

Practice spatial filtering and restoration block diagrams.

Study histogram equalization and Laplacian filtering.

Learn morphological operations and Hough Transform thoroughly.

Practice segmentation and edge detection algorithms.