# Deep Learning For Computer Vision Week 2 Nptel Answers 2024

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## Deep Learning For Computer Vision Week 2 Nptel Answers

Session: JULY-DEC 2024

Q1 ) Which of the following are examples of a high-pass filter? (Select all possible correct options)

a) ⎢−2−2−2−2−2−2−2−2−2⎤⎦⎥

b) ⎡⎣⎢10−120−210−1⎤⎦⎥

⎡⎣⎢121212121⎤⎦⎥

⎡⎣⎢−1−10−101011⎤⎦⎥

Q2. Match the following:
1) Gaussian filter i) Edges found when gradient is low
2) Sobel filter ii) Edges found at zero crossing
3) First derivative of Gaussian iii) Edge smoothing
4) Second derivative of Gaussian iv) Edge detection
v) Edges found when gradient is high

1 → iii, 2 → iv, 3 →i, 4 →ii

1 →iii, 2 →i, 3 →ii, 4 →v

1 →iii, 2 →iv, 3 →v, 4 →ii

1 →iv, 2 →iii, 3 →i, 4 →ii

These are Deep Learning For Computer Vision Week 2 Nptel Answers

Q3.Identify the correct sequence of steps in a Canny edge detection pipeline. Steps are listed below:

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

Q4. In terms of computational efficiency, how does the separability of a 2D convolution kernel affect the filtering process?
It has no effect on efficiency
It allows the convolution to be performed as two 1D convolutions, which is faster
It requires more memory but fewer computations
None of the above

These are Deep Learning For Computer Vision Week 2 Nptel Answers

Q5. Which of the following operations is an example of linear filtering?
Thresholding an image
Histogram equalization
Morphological dilation
Applying a Gaussian blur

Q6.What is the purpose of creating a scale space in SIFT?
To remove noise from the image
To detect features at different scales
To enhance edge detection
To compress the image

These are Deep Learning For Computer Vision Week 2 Nptel Answers

Choose the correct statements from among the following:

Q7.1.For any low-pass or high-pass filter, the sum of the filter coefficients always adds up to 1.
2.Brightness enhancement by image addition is a point operation.
3.k(a∗b)=(ka)∗(kb), where a is the image, b is the filter, k is a scalar and ∗ is the convolution operator.
only 1
1 and 2
only 2
None of the above

Q8.Which of the following statements is false?

Real-world RGB images can be thought of as matrices in continuous space on R3, but the images we store on a computer are discrete.

We can represent an RGB image as a function of the form. f:R3→R where R3
represents image coordinates (channel, height, width) and R represents intensity.

The transformation I^(x,y)=I(x,−y) flip the image I upside down.
Denoising an image through the moving average filter is an example of global operation as opposed to point or local operations.

Q9.Assertion (A): Gabor filters are particularly effective for texture analysis in image processing.
Reason (R): Gabor filters can be tuned to respond to specific frequencies and orientations in an image.
Choose the correct answer from the options below:

Both A and R are true, and R is the correct explanation of A.
Both A and R are true, but R is not the correct explanation of A.
A is true, but R is false.
A is false, but R is true

Q10. Which property is SIFT designed to be invariant to?
Only rotation
Only scale
Rotation and scale
Scale, rotation, and illumination changes

Q11. What is the primary difference between blob detection and corner detection?
Blob detection finds regions, while corner detection finds points
Blob detection finds circles, while corner detection finds rectangle
Corner detection works on color images, while blob detection only works on gray scale
Blob detection requires machine learning, while corner detection doesn’t

Q12.Given is a 3 × 3 image,

The central element after applying linear contrast stretching is:
54
25
13
18