Q1. Which of the following denotes 255 in the binary form?
11111111
00000000
01111111
11111110
Answer: 1 Explanation: In binary form, 255 is represented as 11111111. To know more about how to convert decimal to binary, refer to this link.
Q2. Which of the following OpenCV functions can be used to perform CLAHE?
First we create a CLAHE object using “cv2.createCLAHE()” and then we apply this on the image using .apply() method
cv2.applyCLAHE()
cv2.clahe()
None of the above
Answer: 1 Explanation: In OpenCV, first we create a CLAHE object using “cv2.createCLAHE()” and then we apply this on the image using .apply() method. Refer to this link to know more about this function.
Q3. What is the smallest element of an image?
pixel
dpi
meter
centimeter
Answer: 1 Explanation: In digital image processing, a pixel(or picture element) is the smallest item of information in an image.
Q4. Which of the following OpenCV functions can be used to apply an affine transformation to an image?
cv2.warpAffine()
cv2.affineTransform()
cv2.applyAffine()
cv2.WarpAffine()
Answer: 1 Explanation: In OpenCV, cv2.warpAffine() can be used to apply an affine transformation to an image. Refer to this link to know more.
Q5. Which of the following is an subtractive color model?
RGB
CMYK
Both of the above
None of the above
Answer: 2 Explanation: In subtractive model colors are perceived as a result of reflected light. For instance, Cyan is the complement of red, meaning that the cyan serves as a filter that absorbs red. The amount of cyan applied to a white sheet of paper controls how much of the red in white light will be reflected back from the paper. To know more about subtractive models, refer to this link.
Q6. What type of filters results in image sharpening?
Low Pass filters
High Pass filters
Answer: 2 Explanation: Because high pass filters enhances the high-frequency parts of an image, these results in image sharpening.
Q7. For a skewed image histogram, which technique will can be used for improving the global contrast?
Histogram Equalization
Histogram Matching
Histogram Balancing
None of the above
Answer: 2 Explanation: For skewed image histogram, one reasonable approach can be to manually specify the transformation function that preserves the general shape of the original histogram but has a smoother transition of intensity levels in the skewed areas. This is what we do in Histogram Matching.
Q8. What does the term “Shadows” refers to in a 1D image histogram?
Leftmost part (the black and dark areas)
Rightmost part (light and pure white areas)
Center part (medium grey areas)
There is no such term!!!
Answer: 1 Explanation: Shadows as clear from the name refers to the Leftmost part of the histogram that contains mostly the black and dark areas. To know more about Image Histograms, refer to this link.