B.TechSemester 62024-25Image AnalyticsBCDS061
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Image Analytics (BCDS061) - AKTU Question Paper 2024-25

B.Tech · Semester 6 · Free PDF Download

This is the official AKTU Image Analytics Previous Year Question Paper for B.Tech Semester 6, academic session 2024-25. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 6
Session:2024-25
University:AKTU / UPTU

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Questions Asked in 2024-25

Image Analytics (BCDS061) — complete question paper · 70 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 0 2 x 7 = 1 4
  • a
    What are the main characteristics of a histogram in an image?
  • b
    List different kind of filters
  • c
    What is the Hit-or-Miss transform used for?
  • d
    Name two basic morphological algorithms and their applications
  • e
    Define pseudo color image processing
  • f
    List two preprocessing steps used for boundary detection
  • g
    What are the key components of a convolution neural network (C NN)?
Section BAttempt any three o f t h e f o l l o w i n g : 0 7 x 3 = 2 1
  • a
    What is morphological recons truction? Explain its steps and applications with an example
  • b
    Define image smoothing and image sharpening. How do they dif fer in the context of color image processing?
  • c
    List advantages of using deep learning over traditional patt ern classification techniques
  • d
    What is the purpose of feature extraction in image analysis? 4 K 2
  • e
    Discuss the working and applications of smoothing spatial fi lters with examples like averaging and Gaussian filters
Section CAttempt any one p a r t o f t h e f o l l o w i n g : 0 7 x 1 = 0 7
  • a
    Describe image segmentation using region growing and region splitting & merging methods. Compare their efficiency
  • b
    Describe the steps involved in the Scale-Invariant Feature T ransform (SIFT) algorithm. What makes it robust?
  • a
    Compare and contrast binary morphology and grayscale morphol ogy. Provide use cases for each
  • b
    Describe the minimum-distance classification algorithm. Deri ve the decision rule and illustrate with an example
  • a
    Explain the fundamental steps involved in a digital image pr ocessing system with a block diagram
  • b
    Explain the architecture and learning process of multilayer feed forward neural networks. How does back propagation work?
  • a
    Write and explain a Python/M ATLAB program to perform erosion, dilation, opening, and closing operations on a binary image
  • b
    Explain prototype matching using 2D correlation and discuss its application in template-based recognition
  • a
    Discuss various types of pixel relationships and their impor tance in image processing
  • b
    Explain boundary representation and its significance in obje ct recognition. What are the different techniques used?

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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