Question

Derive Eq.2-69 by applying Eq.2-68 to combined events (Hint: you will need to use the distributive law. )

44. 2.44* Derive Eq. (2-69) by applying Eq. (2-68) to combined events. ( Hint: You will need to use the distributive law in Table 2.1..)

We treat image intensities as random quantities in numerous places in the book. For example, let zal = 0,1,2, , L-1, denote the values of all possible intensities in an M × N digital image. The probability, p(Zn), of intensity levelzk Occurring in the im ge is estimated as (2-67) MN where n is the number of times that intensity z occurs in the image and MN is the total number of pixels. Clearly, L-1 p(zz)-1 (2-68) k=0 Once we have p(zk), we can determine a number of important image characteristics. For example, the mean (average) intensity is given by L-1 (2-69)

Distributive laws (AnB) u C (AUC) (BUC)

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Answer #1

By definition the expected value (or mean) of a discrete random variable X, is a weighted average of the possible values that X can take, each value being weighted according to the probability of that event occurring.

Here the possible values of intensity are 021,L-1 with corresponding probabilities p(z_0),p(z_1), cdots,p( z_{L-1}) respectively.  

Thus we mean intensity

L-1 k=0 L-1 Tnk Σ (17 ) Using 2-67 L-1

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