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NPTEL Deep Learning For Computer Vision Assignment 3 Answers 2022

We Discuss About That NPTEL Deep Learning For Computer Vision Assignment 3 Answers 2022

NPTEL Deep Learning For Computer Vision Assignment 3 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 3 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 3 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

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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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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. Apply the following chain of transformations on the generated coordinates:1. Rotation by 135 degrees.
2. Translation of x coordinate by -2 units and y coordinate by 2 units.
3. Scaling by 2 units.
4. Rotation by 135 degrees.

Ans – D

The following figure represents 5 different set of points(each in different color) plotted on a cartesian plane.
One of them is the correct output that will be produced when we apply the transformations mentioned above to the given input.
Which of the following is the correct output which is produced after applying these transformations?
A
B
C
D
E

Ans -C
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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” segment therein. What is the floor value of the mean?
15
5
17
9

Ans – D
1 point
For this question, please see Question 3 in the iPython notebook(.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Follow the below mentioned steps:Step 1. Given a folder of images, use SIFT to extract their feature descriptors.
Step 2. Perform k-means clustering on the extracted descriptors.
Step 3. Create histograms by taking help of the clusters calculated in the previous step.
Step 4. Estimate 5 nearest neighbours to a given image and add the distances of those 5 neighbours.

What is the range of the sum as calculated in step 4?
0-200
200-400
400-600
600-800

Ans – C
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1 point
Match the following:1) Dense registration through optical flow i) Assumption of rigid transformations
2) Wide baseline spatial matchingof same object ii) Detect different instances
3) Rotation in x − y plane iii) Two degrees of freedom
4) Bag-of-words iv) Aperture problem
v) One degree of freedom
1→iv, 2→i, 3→iii, 4→ii
1→iv, 2→i, 3→v, 4→ii
1→v, 2→ii, 3→iv, 4→i
1→v, 2→i, 3→iii, 4→ii

Ans – B
1 point
Consider the two images A and B below. In A, a dog is present on the left part of the image and in B, the same dog appears on the right part of the image. These two images have ________ bag-of-words representations because bag-of-words representation ________ .same, preserves spatial information
different, preserves spatial information
same, does not preserve spatial information
different, does not preserve spatial information

Ans A
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1 point
Given a set of n images represented by a matrix Z∈Rk×n and a query image q, which one of the following quantities gives the index of best matched image from the pool of n images to the query image q? Here, k is the dimensionality of image representations (e.g. bag-of-words).arg min {ZTq}

arg max {qTZ}

arg min {qTZ}

arg max {ZTq}

Ans – D
1 point
Match the following:
Ans – Not Sure
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