Que. 1) Signature descriptor of an unknown shape is given in the figure. Can you identify the unknown shape?
a) Circle b) Square c) Straight line d) Cannot be predicted
Answer: Updating soon (in progress)
Que. 2) To measure the smoothness, coarseness, and regularity of a region, we use which of the transformations to extract features?
a) Gabor Transformation b) Wavelet Transformation c) Both Gabor and Wavelet Transformation d) None of the above
Answer: Updating soon (in progress)
Que. 3) Suppose the Fourier descriptor of a shape has KK coefficients, and we remove the last few coefficients, using only the first mm (m<Km < K) coefficients to reconstruct the shape. What will be the effect of using truncated Fourier descriptors on the reconstructed shape?
a) We will get a smoothed boundary version of the shape. b) We will get only the fine details of the boundary of the shape. c) The full shape will be reconstructed without any loss of information. d) Low-frequency components of the boundary will be removed from the contour of the shape.
Answer: Updating soon (in progress)
Que. 4) While computing the polygonal descriptor of an arbitrary shape using the splitting technique, which of the following is taken as the starting guess?
a) Vertex joining the two closest points above a threshold on the boundary. b) Vertex joining the two farthest points on the boundary. c) Vertex joining any two arbitrary points on the boundary. d) None of the above
Answer: Updating soon (in progress)
Que. 5) Consider a two-class Bayes’ Minimum Risk Classifier. The probabilities of classes W1W_1 and W2W_2 are P(W1)=0.3P(W_1) = 0.3 and P(W2)=0.7P(W_2) = 0.7, respectively. Given: P(x)=0.545P(x) = 0.545, P(x∣W1)=0.65P(x | W_1) = 0.65, and P(x∣W2)=0.25P(x | W_2) = 0.25. The loss matrix values are [2 1; 1 2]. If the classifier assigns xx to class W1W_1, then which of the following is true?
a) <1.79< 1.79 b) >1.79> 1.79 c) =1.09= 1.09 d) >1.09> 1.09
Answer: Updating soon (in progress)
Que. 6) The Fourier transformation of a complex sequence of numbers s(k)s(k) for k=0,…,N−1k = 0, …, N-1 is given by:
a) s(k)e−j2πk/Ns(k) e^{-j2\pi k/N} b) (missing option) c) (missing option) d) (missing option)
Answer: Updating soon (in progress)
Que. 7) The gray co-occurrence matrix CC of an unknown image is given below. What is the value of the maximum probability descriptor?
Matrix CC:
1 2 2 2
1 3 2 2
2 2 2 2
a) 317\frac{3}{17} b) 112\frac{1}{12} c) 316\frac{3}{16} d) 516\frac{5}{16}
Answer: Updating soon (in progress)
Que. 8) Which of the following is not a boundary descriptor?
a) Polygonal Representation b) Fourier Descriptor c) Signature d) Histogram
Answer: Updating soon (in progress)
Que. 9) We use the gray co-occurrence matrix to extract which type of information?
a) Boundary b) Texture c) MFCC d) Zero Crossing Rate
Answer: Updating soon (in progress)
Que. 10) If the larger values of the gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true?
a) The value of the element difference moment will be low. b) The value of the inverse element difference moment will be low. c) The value of entropy will be very low. d) None of the above
Answer: Updating soon (in progress)
NPTEL Deep Learning Week 1 Assignment Answers (Jan-Apr 2023)
These are NPTEL Deep Learning Week 1 Assignment Answers
Q1) From a pack of 52 cards, two cards are drawn together at random. What is the probability of both the cards being kings? a. 1/15 b. 25/57 c. 35/256 d. 1/221
Q2) For a two class problem Bayes minimum error classifier follows which of following rule? (The two different classes are w₁ and w2, and input feature vector is x) a. Choose w₁ if P(w₁/x) > P(w2/x) b. Choose w₁ if P(w₁)>P(w2) c. Choose w2 if P(w₁)<P(w2) d. Choose w2 if P(w₁/x) > P(w2/x)
Answer: a. Choose w₁ if P(w₁/x) > P(w2/x)
These are NPTEL Deep Learning Week 1 Assignment Answers
Q3) The texture of the region provides measure of which of the following properties? a. Smoothness alone b. Coarseness alone c. Regularity alone d. Smoothness, coarseness and regularity
Answer: d. Smoothness, coarseness and regularity
Q4) Why convolution neural network is taking off quickly in recent times? (Check the options that are true.) a. Access to large amount of digitized data b. Integration of feature extraction within the training process. c. Availability of more computational power d. All of the above.
Answer: d. All of the above.
These are NPTEL Deep Learning Week 1 Assignment Answers
Q5) The bayes formula states : a. posterior = likelihood*prior/evidence b. posterior = likelihood*evidence/prior c. posterior = likelihood * prior d. posterior = likelihood * evidence
Answer: a. posterior = likelihood*prior/evidence
Q6) Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficient and use only first m (m<K) number of coefficient to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape? a. We will get a smoothed boundary version of the shape. b. We will get only the fine details of the boundary of the shape. c. Full shape will be reconstructed without any loss of information. d. Low frequency component of the boundary will be removed from contour of the shape.
Answer: a. We will get a smoothed boundary version of the shape.
These are NPTEL Deep Learning Week 1 Assignment Answers
Q7) The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as a. Signature descriptor b. Polygonal descriptor c. Fourier descriptor. d. Convex Hull
Answer: a. Signature descriptor
Q8) If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true? a. The value of element difference moment will be high. b. The value of inverse element difference moment will be high. c. The value of entropy will be very low. d. None of the above.
Answer: b. The value of inverse element difference moment will be high.
These are NPTEL Deep Learning Week 1 Assignment Answers
Q9) Which of the following is a Co-occurrence matrix based descriptor a. Entropy b. Uniformity c. Signature d. Inverse Element difference moment. e. All of the above.
Answer: e. All of the above.
These are NPTEL Deep Learning Week 1 Assignment Answers
Q10) Consider two class Bayes’ Minimum Risk Classifier. Probability of classes W1 and W2 are, P (w₁) =0.3 and P (w₂) =0.7 respectively. P(x) = 0.55, P (x| w₁) = 0.75, P (x| w2) =0.45 and the loss matrix values are
Find the Risk R (α₂|x). a. 0.42 b. 0.61 c. 0.48 d. 0.39
Answer: a. 0.42
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These are NPTEL Deep Learning Week 1 Assignment Answers
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