JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech IV COMPUTER VISION Previous Question Paper (June - 2024 - R16)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
Define image segmentation and its importance in image analysis.
Answer
Image segmentation is the process of partitioning a digital image into multiple segments or sets of pixels to simplify or change the representation of an image into something more meaningful and easier to analyze. It is crucial because it allows for object detection, recognition, and boundary extraction, serving as the foundational step for high-level image understanding.
Discuss the applications of corner and interest point detection in image registration.
Answer
Corner and interest point detection identify distinctive, invariant features across different images. In image registration, these points act as control points or landmarks. By matching these points between a reference image and a target image, geometric transformations (such as rotation, scaling, or translation) can be estimated to align the images accurately for tasks like panorama stitching or medical image fusion.
Explain how size-based filtering can be used to retain or remove objects.
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Explain how binary images are used to represent shapes.
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Define line localization in the context of the Hough Transform.
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Discuss the trade-offs between accuracy and computational efficiency.
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Describe the principles of shape from shading.
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Differentiate between parametric and spline-based motion models.
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Explain the importance of foreground-background separation in surveillance.
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Explain the significance of sensor fusion in 3D vision systems.
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Part B
Explain advanced morphological operations and their use in texture analysis and pattern recognition.
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Discuss the role of the Harris corner detection algorithm in feature-based image registration and object tracking.
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Explain in detail about co-occurrence matrices and their role in texture characterization.
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How do gradient-based edge detection methods handle noise in images? Discuss their robustness.
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Explain the significance of binary shape analysis in image processing, illustrating how it aids in feature extraction and object identification.
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Describe the process of object labeling and counting in binary images.
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Explain how centroidal profiles are used in shape analysis.
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Explain how connectedness is determined in binary images. How does it affect the segmentation process?
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Explain the principles of line detection using the Hough Transform (HT), detailing how the HT converts image space to parameter space for identifying lines.
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Discuss the foot-of-normal method in Hough Transform-based line detection.
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Explain how the Hough Transform can be applied to locate the iris boundary.
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Describe the RANSAC (Random Sample Consensus) algorithm and explain how RANSAC is used for robust line fitting.
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Explain the significance of triangulation in the process of motion estimation and its role in determining spatial coordinates.
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Explain the concept of optical flow and its role in estimating motion in a sequence of images.
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Explain the concept of parametric motion and its utilization in modeling dynamic objects in 3D vision.
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The concept of translational alignment and its relevance in motion analysis.
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Explain the role of computer vision in locating the roadway in an in-vehicle vision system.
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Explain the concept of the photo album application in computer vision.
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Explain the principles of face detection algorithms and their applications in various fields such as biometrics, security systems, and digital photography.
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Discuss the Active Appearance Models (AAMs) and 3D shape models of faces, explaining how they are used in facial analysis.
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