da5006_2024T3_Q1_NA.pdf

Deep Learning for Computer Vision · Quiz 1 · Sep 2024

Questions and published explanations below are available without starting a test. Some questions may not have a published solution yet.

Question 143 MCQ · 2.0 marks

[[IMAGE:47a7c5713c477bca_1_0]]

A published solution is not available for this question yet.

Question 144 MCQ · 2.0 marks

Which of the following statements is **false?**

Linear contrast stretching is a point operation.
Moving average is an example of local operation.
Convolution in the spatial domain can be obtained through addition in the frequency domain.
All of these.

A published solution is not available for this question yet.

Question 145 MCQ · 2.0 marks

Which of the following statements is True

Gaussian filter is Separable filter because it is non linear.
Median filter is a non separable filter
Gaussian filter is a High Pass filter.
Mean filter is a non separable filter because it is non linear

A published solution is not available for this question yet.

Question 146 MCQ · 2.0 marks

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0.18, 1.5, 1.5, 0.20,−1.5,−0.015
0.92, 2.5, 1, 2.5, 2.5, 1
0.18,−1.5, 0, 0.20, 0,−0.015
0.91, 1, 0, 0.2, 1, 1.7

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Question 147 MCQ · 2.0 marks

Identify the correct sequence of steps in a Canny edge detection pipeline. Steps are listed below: 1. Compute gradient magnitude and direction 2. Connect individual components 3. Smoothen the image 4. Threshold into strong, weak, or no edge 5. Gaussian Filter and Hysteresis 6. Non-maximum suppression 7. Apply derivative to get edges

6→ 1→ 4→ 5 → 2
3→ 1→ 6→ 4 → 2
3→ 5→ 1→ 4 → 2
6→ 8→ 5→ 7 → 2

A published solution is not available for this question yet.

Question 148 MCQ · 2.0 marks

Identify the correct sequence of steps in Scale Invariant Feature Transform (SIFT) method. Steps listed below: 1. Keypoint Descriptor 2. Keypoint Localization 3. Scale-space Extrema Detection 4. Orientation Estimation

4→ 2→ 1→ 3
3→ 2→ 4→ 1
3→ 1→ 2→ 4
None of these.

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Question 149 MCQ · 2.0 marks

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1→ iii, 2→ i, 3 → ii
1→ iii, 2→ ii, 3 → i
1→ ii, 2→ iii, 3 → i
None of these.

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Question 150 MCQ · 2.0 marks

Suppose we have a codebook of 2048 SIFT visual words. We extract 1000 SIFT descriptors (SIFT is a 128-dimensional feature) from a new image. What is the dimensionality of the BoW (Bag-of-Words) descriptor?

1000
128
128 × 2048
2048

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Question 151 MCQ · 2.0 marks

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1→ vi, 2→ iii, 3→ v, 4 → i
1→ i, 2 →iv, 3 → iii, 4→ ii
1→ i, 2 →v, 3 → iii, 4→ iv
1→ iv, 2→ iii, 3→ ii, 4 → i

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Question 152 MCQ · 2.0 marks

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5 × 5
7 × 7
9 × 9
3 × 3

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Question 153 MSQ · 2.0 marks

Which of the following statements are **false?**

Momentum in optimization can cause oscillations around minima when encountering flat regions or saddle points due to the dominance of the momentum term over the small gradient.
Stochastic Gradient Descent (SGD) with its inherent noise can be beneficial in escaping local minima and saddle points, provided that there is enough gradient information in the neighborhood.
Adagrad is an optimization algorithm introduced to overcome the diminishing learning rate problem in techniques like RMSProp.
ADAM is introduced to solve problems in RMSProp by combining RMSProp and Adagrad techniques.

A published solution is not available for this question yet.

Question 154 MSQ · 2.0 marks

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[[IMAGE:47a7c5713c477bca_5_6]]
[[IMAGE:47a7c5713c477bca_5_7]]
[[IMAGE:47a7c5713c477bca_5_8]]
[[IMAGE:47a7c5713c477bca_5_9]]

A published solution is not available for this question yet.

Question 155 MSQ · 2.0 marks

[[IMAGE:47a7c5713c477bca_6_10]]

[[IMAGE:47a7c5713c477bca_6_11]]
[[IMAGE:47a7c5713c477bca_6_12]]
[[IMAGE:47a7c5713c477bca_6_13]]
[[IMAGE:47a7c5713c477bca_6_14]]

A published solution is not available for this question yet.

Question 156 MSQ · 2.0 marks

Which of the following statements are **true**? (Select all that apply)

[[IMAGE:47a7c5713c477bca_6_15]]
[[IMAGE:47a7c5713c477bca_6_16]]
[[IMAGE:47a7c5713c477bca_6_17]]
[[IMAGE:47a7c5713c477bca_7_18]]

A published solution is not available for this question yet.

Question 157 MSQ · 2.0 marks

Which of the following are **true**? (Select all that apply)

[[IMAGE:47a7c5713c477bca_7_19]]
[[IMAGE:47a7c5713c477bca_7_20]]
[[IMAGE:47a7c5713c477bca_7_21]]
[[IMAGE:47a7c5713c477bca_7_22]]

A published solution is not available for this question yet.

Question 158 SHORT_TEXT · 0.5 marks

The gradient of an image I, points in the direction of most rapid change in intensity, taking image derivatives accentuates (a) ____________ frequencies and hence amplifies noise, since the proportion of noise to signal is larger at (b) __________ frequencies. The solution to this is (c) _________ the image prior to computing gradients. Based on the above data, answer the given subquestions.

Enter the correct answer for (a). **NOTE:** Enter the exact answer without any space in the beginning or at the end.

A published solution is not available for this question yet.

Question 159 SHORT_TEXT · 0.5 marks

Enter the correct answer for (b). **NOTE:** Enter the exact answer without any space in the beginning or at the end.

A published solution is not available for this question yet.

Question 160 SHORT_TEXT · 1.0 marks

Enter the correct answer for (c). **NOTE:** Enter the exact answer without any space in the beginning or at the end.

A published solution is not available for this question yet.

Question 161 SHORT_TEXT · 1.0 marks

In Canny edge detection, large value of the Gaussian kernel spread, σ leads to (a) __________ edges, and small value of σ leads to (b) __________ edges. Based on the above data, answer the given subquestions.

Enter the correct answer for (a). **NOTE:** Enter the exact answer without any space in the beginning or at the end.

A published solution is not available for this question yet.