11. For the transformation T(r) = [∫0r pr(w) dw], r is the gray value of the input image, pr(r) is the PDF of random variable r and w is a dummy variable. If the PDF is always positive and the function under integral gives the area under the function, the transformation is said to be __________
A. Single valued
B. Monotonically increasing
C. All of the mentioned
D. None of the mentioned
12. The transformation T (rk) = ∑k(j=0) nj /n, k = 0, 1, 2, …, L-1, where L is max gray value possible and r-k is the kth gray level, is called _______
A. Histogram linearization
B. Histogram equalization
C. All of the mentioned
D. None of the mentioned
13. If the histogram of the same images, with different contrast, are different, then what is the relation between the histogram equalized images?
A. They look visually very different from one another
B. They look visually very similar to one another
C. They look visually different from one another just like the input images
D. None of the mentioned
14. The technique of Enhancement that has a specified Histogram processed image as result, is called?
A. Histogram Linearization
B. Histogram Equalization
C. Histogram Matching
D. None of the mentioned
15. In Histogram Matching r and z are the gray level of input and output image and p stands for PDF, then, what does pz(z) stand for?
A. Specific probability density function
B. Specified pixel distribution function
C. Specific pixel density function
D. Specified probability density function
16. Inverse transformation plays an important role in which of the following Histogram processing Techniques?
A. Histogram Linearization
B. Histogram Equalization
C. Histogram Matching
D. None of the mentioned
17. In Histogram Matching or Specification, z = G-1[T(r)], r and z are the gray level of input and output image and T & G are transformations, to confirm the single value and monotonous of G-1 what of the following is/are required?
A. G must be strictly monotonic
B. G must be strictly decreasing
C. All of the mentioned
D. None of the mentioned
18. Which of the following histogram processing techniques is global?
A. Histogram Linearization
B. Histogram Specification
C. Histogram Matching
D. All of the mentioned
19. What happens to the output image when the global Histogram equalization method is applied to the smooth and noisy area of an image?
A. The contrast increases a little bit with the considerable enhancement of noise
B. The result would have a fine noise texture
C. All of the mentioned
D. None of the mentioned
20. Let us suppose an image containing a quite small square under a large dark square with both having very close gray level values. If an image contains some of this such that the small squares can’t be visualized and some noise blurred enough to reduce its noise content as shown in fig. below, Which of the following method would be preferred for obtaining the small square clear enough?
A. Global histogram equalization
B. Local histogram equalization
C. All of the mentioned
D. None of the mentioned