AKTU Image Analytics (BCDS061) Question Paper 2025-26

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. What are the fundamental steps in an image processing system?
2. Write four difference between smoothing and sharpening filters.
3. Define morphological image processing.
4. Define thinning and thickening.
5. What is meant by full color image processing?
6. What is region splitting and merging?
7. Define texture descriptors.

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Explain the role of filters in noise reduction and edge enhancement.
2. Explain advantages and limitations of color-based vs grayscale segmentation.
3. Discuss applications of Scale-Invariant Feature Transform (SIFT) in image matching and recognition.
4. Discuss the limitations of Euclidean distance classifier and suggest improvements.
5. Discuss how class-conditional probability density functions affect Bayes classifier performance.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the effect of different structuring elements on morphological operations.
2. Discuss different types of noise in color images and their removal.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Discuss applications of morphology in noise removal and shape analysis.
2. Explain multilayer feedforward neural networks with backpropagation.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe architecture and working of deep convolutional neural networks (CNNs).
2. Compare SIFT feature matching vs correlation-based matching in terms of robustness.

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe color fundamentals and different color models (RGB, CMY, HSV).
2. Explain the complete pattern recognition pipeline from feature extraction to classification.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Derive and explain image negative transformation and its applications.
2. Explain advantages of Fourier descriptors over shape numbers.

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