a filter which can be written as a product of two more simple filters
a filter that can separate noise from other features
a filter which can be written as a sum of two more simple filters
There is no such term!!!
Answer: 1 Explanation: A separable filter is the one that can be written as a product of two more simple filters.
Q2. Which of the following OpenCV functions can be used to perform Adaptive Thresholding?
cv2.adaptiveThreshold()
cv2.threshold()
cv2.adaptThreshold()
cv2.thresh()
Answer: 1 Explanation: In OpenCV, cv2.adaptiveThreshold() can be used to perform Adaptive Thresholding. Refer to this link to know more.
Q3. In some cases, the Adaptive Histogram Equalization technique tends to over-amplify the noise. Which technique is used to solve this problem?
Histogram Specification
CLAHE
SWAHE
All of the above
Answer: 2 Explanation: The Adaptive Histogram Equalization (AHE) technique tends to over-amplify the noise so to avoid this contrast limiting is applied and this method is known as Contrast Limited Adaptive Histogram Equalization (CLAHE).
Q4. What is Ringing effect in image processing?
a rippling artifact near sharp edges
a rippling artifact in smooth areas
In this, the filter rings(warns) about the noise
There is no such effect!!!
Answer: 1 Explanation: In image processing, ringing effect refers to a rippling artifact near sharp edges. To know more about this effect, refer to this link.
Q5. What is local contrast enhancement?
In this, we divide the image into small regions and then perform contrast enhancement on these regions independently
In this, we divide the image into small regions and then perform contrast enhancement on all these regions using same transformation function
In this, we simply perform contrast enhancement on the entire image
None of the above
Answer: 2 Explanation: As clear from the name, in local contrast enhancement we divide the image into small regions and then perform contrast enhancement on these regions independently. The transformation function for this is derived from the neighborhood of every pixel in the image.
Q6. In the Gaussian filter, what is the relation between standard deviation and blurring?
Larger the standard deviation more will be the blurring
Larger the standard deviation less will be the blurring
No relation!!!
Answer: 1 Explanation: In Gaussian filter, larger the standard deviation more will be the blurring.
Q7. Which of the following are the common uses of image gradients?
Edge detection
Feature Matching
Both of the above
None of the above
Answer: 3 Explanation: Image gradients can be used for both Edge Detection (for instance in Canny Edge Detector) and Feature Matching.
Q8. How does the change in filter size affects blurring? Assume the filter is a smoothing filter.
Blurring increases with decrease in the filter size
Blurring decreases with increase in the filter size
Blurring increases with increase in the filter size
There is no effect of filter size on blurring!!!
Answer: 3 Explanation: As we increase the filter size, Blurring also increases.