Section A – Short Concept Questions (Description)
Section A contains short conceptual questions related to the fundamentals of Digital Image Processing (DIP). These questions are designed to test the student’s basic understanding of core concepts such as sampling, pixels, color components, noise models, and image processing elements.
Students are expected to provide brief and precise answers explaining important terms used in image processing systems. Topics in this section include the structure of digital images, the human visual system, and the fundamental components of image processing systems.
The questions also focus on basic terminology such as bit plane slicing, pixel definition, path representation in images, Mach bands, and noise models. Understanding these basic concepts is important because they form the foundation for advanced image processing techniques such as image restoration, segmentation, and pattern recognition.
Section A mainly tests conceptual clarity and the ability to define technical terms correctly.
Questions for Section A
Define the sampling process in digital image processing.
What is bit plane slicing in image processing?
Name any two noise models used in image processing.
What do hue and saturation represent in color models?
What is meant by a pixel?
What are the elements of a Digital Image Processing system?
What are Mach bands in visual perception?
What is the Kalman theorem?
How are cones and rods distributed in the retina?
What is meant by a path in digital images?
Section B – Descriptive Questions (Description)
Section B includes descriptive questions that require detailed explanations of important image processing techniques and systems. Students must explain theoretical concepts, mathematical models, and system architectures related to digital image processing.
This section covers topics such as stochastic image models, stereo imaging and visual perception, image quantization, discrete Fourier transform (DFT), image restoration systems, segmentation techniques, and pattern recognition methods.
Students are expected to explain the working principles of these techniques and provide diagrams or mathematical expressions where required. For example, they may need to derive the DFT expression for an image, explain the region growing technique in image segmentation, or describe the architecture of a digital image restoration system.
These questions test the student’s ability to understand and explain complex technical topics in a structured way.
Questions for Section B
Justify that an image can be considered a stochastic process.
Explain stereo imaging elements of visual perception.
What is an image quantizer? Explain different types of image quantizers and their advantages.
Derive the expression for the DFT of an NxN image and explain its properties.
Draw the block diagram of a digital image restoration system and explain its working.
What is image segmentation? Why is it required? Explain the region growing technique.
Explain how pattern recognition methods are used for rapid object recognition.
Draw the block diagram of signature verification and explain its working.
Section C – Analytical / Long Answer Questions (Description)
Section C contains long analytical questions that require deeper understanding of image processing algorithms and techniques. Students must provide detailed explanations, mathematical derivations, and practical applications of advanced image processing methods.
Topics in this section include Hough transform, texture analysis, topological analysis, pseudo color enhancement, fingerprint classification, run-length coding, and moment invariants used in image recognition.
These questions test the student’s ability to analyze image processing methods and explain how these techniques are used in real-world applications such as biometric systems, object detection, and pattern recognition.
Answers should include theoretical explanations, diagrams, and mathematical formulations where applicable.
Questions for Section C
Explain the Hough Transform and discuss topological and texture analysis techniques.
Write short notes on:
Pseudo color enhancement
Fingerprint classification
Run length coding
Define the moment for a two-dimensional signal f(x, y) > 0. Explain how different order moments are useful in image recognition and describe moment invariants.
