October 1, 2023
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NPTEL Deep Learning For Computer Vision

NPTEL Deep Learning For Computer Vision Assignment 1 Answers 2022

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NPTEL Deep Learning For Computer Vision Assignment 1 Answers 2022 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

Are you looking for the Assignment Answers to NPTEL Deep Learning For Computer Vision Assignment 1 Answers 2022? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “ NPTEL Deep Learning For Computer Vision Assignment 1 Answers 2022”

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

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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-1-2022.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
For this question, please see Question 1 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Now, which of the following is the GBR representation of the RGB image ‘golden retriever.jpg’ in programming question 1?Ans – B

1 point
For this question, please see Question 2 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segments therein. The shape of the ‘human mitosis’ image after applying a 5×5 mean filter is:
(512,512)
(510,510)
(508,508)
(507,507)

Ans – B

For this question, please see Question 3 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segments therein. The median value of the ‘human_mitosis’ image before and after applying the 5 × 5 mean filter is a and b respectively.
(a)
0.5 points
(b)
0.5 points

Ans -A
1 point
A 5×5 kernel generates an output image of dimension 10×10 after convolution. The (approximate) number of computations performed to obtain this output image is:
25
100
250
2500
Ans -c
1 point
When we perform SVD on a 3 × 3 2D kernel, we obtain the following rectangular diagonal matrix ∑=⎛⎝⎜30000100000⎞⎠⎟. Is the 2D kernel separable?
Yes
No
Ans – A
1 point
Consider a 4 × 4 image, where each pixel uses a 4-bit signed representation (range between −23 to 23 − 1). Reverse the contrast of the following image:
⎛⎝⎜⎜⎜3041713602301512⎞⎠⎟⎟⎟⎛⎝⎜⎜⎜2−1306025−112−10401⎞⎠⎟⎟⎟

⎛⎝⎜⎜⎜−21−30−60−2−51−1−210−40−1⎞⎠⎟⎟⎟

⎛⎝⎜⎜⎜−4−1−5−2−8−2−4−7−1−3−4−1−2−6−2−3⎞⎠⎟⎟⎟

⎛⎝⎜⎜⎜4152824713412623⎞⎠⎟⎟⎟

Ans – C
1 point
Given the 3 × 3 4-bit grayscale image below:
⎡⎣⎢777648635⎤⎦⎥
what is the central element after applying linear contrast stretching? (Round off to the nearest integer)
3
4
5
6
Ans – A
Map the following image operations to their types – provide your answer as integers where 0,1 and 2 correspond to point, local and global operation respectively. (Assume that the image size is 10×10)
Mean of an image:
0.25 points
Convolution with kernel of size 1 × 1:
0.25 points
Cross-correlation with kernel size 3 × 3:
0.25 points
Flip an image by 180o:
0.25 points
Ans – A
Consider the 3 × 3 average filter given below:
1?⎛⎝⎜222242222⎞⎠⎟
The correct normalizing factor is given by:
Ans – A
1 point
1 point
Match the following:1) Global operation i) Inner product of two 1D kernels
2) Histogram equalization ii) Fourier transform
3) Separable 2D kernel iii) Noise spread
4) Size of Gaussian kernel iv) Contrast stretching
v) Gaussian kernel
1→ ii, 2→ iv, 3→ v, 4→ iii
1→ ii, 2→ iv, 3→ i, 4→ iii
1→ ii, 2→ iv, 3→ i, 4→ v
1→ ii, 2→ iv, 3→ iii, 4→ v

Ans – A
1 point
Choose the incorrect statements from among the following:
For any low-pass or high-pass filter, the sum of the filter coefficients always adds up to 1.
Brightness enhancement by image addition is a point operation.
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 3
only 2
None of the above
Ans – D
1 point
Which of the following is an example of a high-pass filter?
(Select all possible correct options)⎡⎣⎢121212121⎤⎦⎥

⎡⎣⎢⎢⎢⎢⎢⎢1111111111110111111111111⎤⎦⎥⎥⎥⎥⎥⎥

⎡⎣⎢111111111⎤⎦⎥

⎡⎣⎢−1−10−101011⎤⎦⎥

Ans – C
1 point
Match the following images with their corresponding Fourier Transforms:1→ iii, 2→ iv, 3→ i, 4→ ii
1→ ii, 2→ iii, 3→ i, 4→ iv
1→ i, 2→ ii, 3→ iii, 4→ iv
1→ iii, 2→ iv, 3→ ii, 4→ i

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