During the double thresholding step in Canny edge detection, the high threshold is set at 100 and
the low threshold is 40. A pixel gradient magnitude of 65 will be classified as:
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Strong edge
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Weak edge
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Non-edge
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Ambiguous edge
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Momentum in optimization can cause oscillations around minima when
encountering flat regions or saddle points due to the dominance of the momentum term over the
small gradient.
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Stochastic Gradient Descent (SGD) with its inherent noise can be beneficial in
escaping local minima and saddle points, provided that there is enough gradient information in
the neighborhood.
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Adagrad is an optimization algorithm introduced to overcome the diminishing
learning rate problem in techniques like RMSProp.
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ADAM is introduced to solve problems in RMSProp by combining RMSProp and
Adagrad techniques.
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The gradient of an image I points in the direction of the most rapid change in intensity. Taking
image derivatives accentuates (a)_________________ frequencies and hence amplifies noise, since the
proportion of noise to signal is larger at (b) _________________frequencies. The common solution is
to smooth the image prior to computing gradients.
Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (a):
**NOTE:** Enter the exact answer without any extra space in the beginning or at the end.
A published solution is not available for this question yet.
The gradient of an image I points in the direction of the most rapid change in intensity. Taking
image derivatives accentuates (a)_________________ frequencies and hence amplifies noise, since the
proportion of noise to signal is larger at (b) _________________frequencies. The common solution is
to smooth the image prior to computing gradients.
Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (b):
**NOTE:** Enter the exact answer without any extra space in the beginning or at the end.
A published solution is not available for this question yet.
In Canny edge detection, a large value of the Gaussian kernel spread σ leads to (a)________________
edges, while a small value of σ leads to (b)_________________ edges.
Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (a) :
**NOTE:** Enter the exact answer without any extra space in the beginning or at the end.
A published solution is not available for this question yet.
In Canny edge detection, a large value of the Gaussian kernel spread σ leads to (a)________________
edges, while a small value of σ leads to (b)_________________ edges.
Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (b) :
**NOTE:** Enter the exact answer without any extra space in the beginning or at the end.
A published solution is not available for this question yet.
You have a visual codebook with 1500 visual words. From a test image, you extract 1200 SIFT
descriptors, each of 128 dimensions. After assigning each descriptor to its nearest visual word,
you compute the Bag-of-Words histogram by counting descriptor assignments.
Based on the above data, answer the given subquestions.
What is the dimensionality of the Bag-of-Words (BoW) feature vector for this
image?________________
A published solution is not available for this question yet.
You have a visual codebook with 1500 visual words. From a test image, you extract 1200 SIFT
descriptors, each of 128 dimensions. After assigning each descriptor to its nearest visual word,
you compute the Bag-of-Words histogram by counting descriptor assignments.
Based on the above data, answer the given subquestions.
If instead, you concatenate all the 1200 descriptors into a single vector (without using BoW), what
will be the dimensionality of that vector?__________________
A published solution is not available for this question yet.