NPTEL Deep Learning For Computer Vision Assignment 2 Answers 2022

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NPTEL Deep Learning For Computer Vision

ABOUT THE COURSE :

The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users. This course will introduce the students to traditional computer vision topics, before presenting deep learning methods for computer vision. The course will cover basics as well as recent advancements in these areas, which will help the student learn the basics as well as become proficient in applying these methods to real-world applications. The course assumes that the student has already completed a full course in machine learning, and some introduction to deep learning preferably, and will build on these topics focusing on computer vision.

Next Week Assignment Answers

This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score

Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
Proctored Exam score =50% of the proctored certification exam score out of 100

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.

If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100.

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Starter code for Question 1 to Question 3 of this assignment is provided in DL4CV-Assignment-2-2022.ipynb.
Use Python 3.x to run the notebook. As instructed in the notebook, write your code only in between the lines ’YOUR CODE STARTS HERE’ and ’YOUR CODE ENDS HERE’.
Do not change anything else in the code; if you do, the answers you are supposed to get at the end of this assignment might be wrong.
Read documentation of each function carefully.
All the best!

Ans – D
1 point
Sobel Filter: What are index of the min and max values of the Sobel filter response at the end of Question 1 in the notebook file?
(52, 147), (111, 26)
(0, 0), (108, 107)
(56, 152), (116, 18)
(51, 143), (114, 24)

Ans – B
1 point
Laplacian Filter: What are index of the min and max values of the Laplacian filter response at the end of Question 2 in the notebook file?
(4, 117), (111, 26)
(49, 176), (110, 27)
(203, 136), (17, 136)
(10, 104), (106, 22)

Ans – B
1 point
Harris Corner Detection: How many corners are detected in the image at the end of Question 3 in the notebook file?
1491
807
1068
796

Ans – C
1 point
Which one of the following statements is true?

MSER regions are the ones with Ψ(.) greater than the userdefined thresholds, where Ψ is a function of cardinalities of a region at a given level

Consider the Laplacian pyramid(L1,L2,…) and the Gaussian pyramid (G1,G2,…). We can write: L1=G1+ Smooth(Upsample(Downsample(Smooth(G1))))

The gradient orientation for a horizontal edge is 0∘
Automatic scale selection in a scale-invariant interest point detector uses fixed window size with a Gaussian pyramid

Ans – A

1 point
Consider the following statements, and state which of the provided options are true.
SURF is better than SIFT in rotation invariance and blur
Harris corner detector can handle changes in illumination but SIFT cannot handle this
SIFT uses LoG while the Harris corner detector uses only gradient, and SURF uses box filters
SIFT descriptors with dimensions lower than 128 perform pretty well across a range of matching tasks
SIFT is better than SURF for changes in illumination and viewpoints
1, 2, 4 and 3
1, 3 and 5
2 and 3 only
1, 2, 4 and 5

Ans – C
1 point
Which of the following statements are false? Select ALL correct options.
Prewitt filter uses zero-crossings to identify the edges
Harris corner detection is invariant to contrast changes
Translation of an image changes the eigenvalues, which in turn affects the cornerness score
Eigenvectors doesn’t rotate when the image patch is rotated

Ans – D
1 point
Consider the Harris corner detection algorithm, where the hyperparameter k=0.15. The auto-correlation is given by E=UTXU, where X can be one among the following (A or B or C):

A=[7115], B=[8008], C=[6336]

Find the X that gave a cornerness response greater than 10.
A,C
B,C
A,B
B

Ans – B
1 point
Match the following with the corresponding eigenvalues (assume that λ1 measures change across a row of the image or the x-axis, and λ2 measures change across a column or y-axis):

1) Vertical Edge i) λ1 and λ2 << ∞
2) Flat ii) λ2 >> λ1
3) Corner iii) λ1 and λ2 >> 0
4) Horizontal Edge iv) λ1 >> λ2
v) λ1 and λ2 = 0
1→iii, 2→iv, 3→i,4→v
1→ii, 2→i, 3→iv,4→iii
1→iii, 2→i, 3→iv,4→ii
1→iv, 2→i, 3→iii,4→ii

Ans – A
1 point
Which of the following statements are true? Select ALL correct options.
LoG can be used to find the edges as well as for blob detection
Maximally Stable Extremal Regions is only affine-invariant
Large contrast changes are difficult to localize
Sobel filter helps us fetch the texture information

Ans – D
1 point
Which of the following is the convolution filter associated with the following expression:
10f(x−1,y−1)+20f(x,y−1)+30f(x,y)+40f(x+1,y)−50f(x−1,y+1)−60f(x+1,y+1)2

⎡⎣⎢100−5020300040−60⎤⎦⎥

⎡⎣⎢100502030004060⎤⎦⎥

⎡⎣⎢50−2510150020−30⎤⎦⎥

⎡⎣⎢50251015002030⎤⎦⎥

Ans – C
1 point
Given a 2×2 matrix M=[1221], where M is the second moment matrix of edge pixels that is obtained while applying the Canny edge detector, find the measure of straightness.
Hint: The largest eigenvalue should be

Ans – Not Sure

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NPTEL Deep Learning For Computer Vision

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