Section A – Basic Concept Questions (Description)
Section A consists of short conceptual questions related to the basic principles of Digital Image Processing. These questions are designed to evaluate a student’s understanding of fundamental concepts such as sampling, quantization, smoothing, sharpening, and grey level slicing techniques.
Students are expected to give brief explanations of technical terms used in image processing systems. This section also includes concepts related to biometric recognition, texture analysis, fuzzy logic, and Mach bands, which are important for understanding how images are analyzed and interpreted.
The questions in this section mainly focus on theoretical definitions and basic explanations. These concepts form the foundation of more advanced topics such as image restoration, segmentation, pattern recognition, and image compression.
A clear understanding of these fundamental topics is important because they help students learn how digital images are processed, enhanced, and interpreted in computer vision and artificial intelligence applications.
Questions for Section A
Explain sampling and quantization in digital image processing.
What is 3D image processing?
Explain different grey level slicing techniques.
What is meant by smoothing and sharpening in image processing?
How can biometric features be used for recognition?
Explain topological and texture analysis in brief.
What is signature verification?
What are Mach Bands?
What is isomorphism in image processing?
Define fuzzy logic.
Section B – Descriptive Questions (Description)
Section B includes descriptive and analytical questions that require detailed explanations of important techniques used in digital image processing. Students must explain theoretical concepts, mathematical expressions, and system architectures related to image analysis and recognition.
Topics in this section include Discrete Cosine Transform (DCT), digital image restoration systems, image segmentation methods, wavelet and Fourier analysis, and Gaussian filtering techniques. Students may also be asked to explain block diagrams and working principles of systems such as signature verification systems and image restoration systems.
These questions test the student’s ability to analyze and explain complex image processing methods. Students are often expected to provide diagrams, mathematical derivations, and practical examples while answering these questions.
Questions for Section B
Derive the mathematical expression for the Discrete Cosine Transform (DCT) and explain its properties.
Draw the block diagram of a signature verification system and explain its working.
Explain the block diagram of a digital image restoration system and discuss the Wiener filter.
Explain match-based segmentation and differentiate between supervised and unsupervised evaluation.
Discuss Fourier, wavelet, and principal component analysis used in image analysis.
Show that a two-dimensional Gaussian function is separable while the Laplacian of Gaussian operator is not separable.
Write short notes on:
Fingerprint classification
Text recognition
What is an image quantizer? Explain its advantages.
Section C – Long Analytical Questions (Description)
Section C contains long analytical questions that require detailed explanations and comparisons of advanced image processing techniques. These questions evaluate the student’s ability to apply theoretical knowledge to complex image processing problems.
Students may be asked to compare filtering techniques, explain histogram-based image enhancement methods, and discuss image compression techniques used for efficient storage and transmission of images.
This section also includes questions related to sampling theory, such as Nyquist rate, aliasing, and foldover frequency. These concepts are essential in understanding how digital images are sampled and reconstructed without losing important information.
Answers in this section should be detailed and structured, often including mathematical explanations, diagrams, and examples.
Questions for Section C
Differentiate between Wiener filtering and inverse filtering.
Differentiate between linear interpolation and bicubic interpolation.
What is a histogram in image processing and how is it used? Explain histogram specification with an example.
Discuss various image compression techniques.
What is Nyquist rate, aliasing, and foldover frequency in image sampling? Explain how an image is reconstructed from its samples.
