Question 2
What is the role of the Gaussian blur in the Canny edge detection algorithm, and how does the choice of the standard deviation parameter affect the performance of edge detection? Which option do you think best describes the role of the Gaussian blur and its relationship with the standard deviation parameter in the Canny edge detection algorithm?
The Gaussian blur helps to reduce noise in the image and smooth out pixel intensity variations. The standard deviation parameter controls the amount of blurring applied, with higher values resulting in more aggressive smoothing, which may lead to loss of edge detail.
The Gaussian blur enhances edge contrast in the image and amplifies pixel intensity gradients. The standard deviation parameter determines the width of the blur kernel, with larger values resulting in sharper edges and finer details preserved.
The Gaussian blur serves to highlight high-frequency components in the image and accentuate edge boundaries. The standard deviation parameter dictates the level of detail preserved, with smaller values preserving finer details but potentially amplifying noise.
The Gaussian blur functions to dilate edge regions in the image and expand the edge boundaries. The standard deviation parameter controls the extent of dilation, with higher values resulting in broader edge regions but also increasing the risk of false edge detections.
Question 3
Which property of the SIFT descriptor is primarily responsible for its resilience to changes in scale, viewpoint, and illumination?
Scale space representation
Histogram of gradients
Keypoint detection
Orientation assignment
41 more questions in this paper
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More on the Deep Learning for Computer Vision End Term 28 Apr 2024 paper
The IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) End Term paper sat on 28 Apr 2024, in the January 2024 term: 44 questions for 72 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Deep Learning for Computer Vision End Term 28 Apr 2024 at a glance |
|---|---|
| Term | January 2024 term |
| Subject | Deep Learning for Computer Vision |
| Course code | BSDA5006 |
| Questions | 44 |
| Marks | 72 |
| Duration | 180 min |
| MCQ | 25 |
| MSQ | 1 |
| Numerical | 15 |
| Written | 3 |
| Official paper | IIT M DEGREE AN EXAM QDB3 28 Apr 2024 |
| Negative marking | No negative marking. |
| Updated |