Important RGPV Question
CS-803 (A) Image Processing and Computer Vision
VIII Sem, CSE
UNIT 1-Introduction To Computer Vision And Image Processing (CVIP)
Q.1) Discuss the history and evolution of Computer Vision and Image Processing (CVIP).
(RGPV Dec 2024)
Q.2) Explain the significance of image representations and image filtering.
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Q.3) Define morphological image processing and describe its primary operations.
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Q.4) Define region analysis and describe the concept of spatial moments.
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Q.5) Discuss the evolution and history of Computer Vision and Image Processing (CVIP). How have advancements in technology influenced the development of CVIP?
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Q.6) Explain the steps involved in image conditioning, labeling, and grouping for image recognition.
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Q.7) Define morphological image processing and describe the operations of dilation and erosion on binary images.
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Q.8) Describe thinning and thickening operations. Provide an example of an application where these operations are useful.
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Q.9) Explain the concept of computer vision which is mandatory in image processing.
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Q.10) Discuss various color vision models.
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Q.11) What are the components of digital image processing system? Explain each in detail.
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Q.12) Distinguish between spatial and frequency domain image enhancement.
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UNIT 2-Image Representation And Description
Q.1) Differentiate between dilation and erosion in binary and grayscale images.
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Q.2) What is image segmentation? Discuss thresholding and connected component labeling techniques.
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Q.3) Describe the split-and-merge technique used in hierarchical segmentation.
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Q.4) Explain edge detection and the Hough transform in area extraction.
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Q.5) Describe the least-square fitting technique in curve fitting and its applications.
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Q.6) What are boundary and region descriptors in image processing? Explain their importance in image analysis.
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Q.7) Explain the concept of segmentation in binary machine vision. Discuss the methods of thresholding and connected component labeling.
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Q.8) Illustrate how the image is digitized by sampling and quantization process?
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Q.9) Show the various techniques in frequency domain to enhance a image with necessary examples.
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Q.10) Explain Hough transforms with the help of suitable derivations.
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Q.11) Explain how the line is detected in the image and give the masks that are used to detect it?
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Q.12) Explain Boundary descriptors in detail with a neat diagram.
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Q.13) Explain the segmentation techniques that are based on finding the regions directly.
(RGPV May 2024)
UNIT 3-Region Analysis
Q.1) Explain the role of signature properties and shape numbers in boundary analysis.
(RGPV Dec 2024)
Q.2) Explain spatial moments and gray-level moments in region analysis. How are these properties used in object recognition?
(RGPV June 2025)
Q.3) Explain the distance relational approach in the context of image matching. Compare it with ordered structural matching.
(RGPV June 2025)
UNIT 4-Facet Model Recognition
Q.1) What is the backtracking algorithm? Explain its application in solving consistency labelling problems.
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Q.2) Describe perspective projective geometry and inverse perspective projection.
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Q.3) Describe the facet model recognition method. How is it applied to shape classification in images?
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Q.4) Explain projective geometry and its role in interpreting 3D objects from 2D images.
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Q.5) Illustrate the back-tracking algorithm used in shape recognition. Provide a scenario where this algorithm is applied.
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Q.6) Explain inverse perspective projection algorithm.
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UNIT 5-Knowledge Based Vision
Q.1) What is knowledge-based vision and how does it support object recognition?
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Q.2) Explain the Hough transform in the context of simple object recognition methods.
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Q.3) Define Principal Component Analysis (PCA) and its application in feature extraction.
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Q.4) Describe how neural networks are used in image shape recognition.
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Q.5) Explain the Hough transform and discuss how it is used in detecting simple objects in images.
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Q.6) What is Principal Component Analysis (PCA)? Describe its role in feature extraction for object recognition.
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Q.7) Discuss the procedure for conversion from RGB color model to HIS color model.
(RGPV May 2024)
Q.8) Explain the use of neural network structures for pattern recognition with an example.
(RGPV May 2024)
Extra Questions-
Q.1) Define:
i) Inverse perspective projection
ii) Shape numbers used in boundary analysis
(RGPV June 2025)
Q.2) Write short note on (any two)–
a) Machine learning and neural networks in image shape recognition.
b) Knowledge-Based Vision.
c) Curve Fitting
(RGPV June 2025)
Q.3) Define briefly the following terms:
i) Image restoration
iii) Segmentation
iv) Morphological processing
(RGPV May 2024)
Q.4) Write brief notes:
a) Feature extraction
b) Knowledge Representation
c) Backtracking algorithm
(RGPV May 2024)
— Best of Luck for Exam —
