By Ferdinand van der Heijden
What makes this ebook distinctive is that in addition to info on snapshot processing of gadgets to yield wisdom, the writer has committed loads of notion to the size issue of photo processing. this is often of direct functional use in different sectors from business caliber and robotics to medication and biology.
Read or Download Image Based Measurement Systems: Object Recognition and Parameter Estimation (Design & Measurement in Electronic Engineering) PDF
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Additional resources for Image Based Measurement Systems: Object Recognition and Parameter Estimation (Design & Measurement in Electronic Engineering)
Example text
In other words, the joint probability density of n( x , y ) and n( x + a, y + b) depends only on ( a, b) . 5) The notation on the right hand side is convenient if the process is stationary. If not, the notation on the left hand side is needed. e. a or b are sufficiently large), it is likely that the statistical behavior of n( x , y ) and n( x + a, y + b) is independent from each other: knowledge of the irradiance at ( x, y ) does not supply any knowledge about the irradiance at ( x + a, y + b) .
E. 1b. 1a. 3. 4 gives an example that shows how these models may be used to quantify the performance of a segmentation algorithm. 1 FIRST ORDER STATISTICS The common factor in the models discussed next is that the irradiance within each region is assumed to consist of two components: one is deterministic, the other is stochastic. The deterministic component is due to a regular irradiation of the object surface and to a reflectance distribution which is smooth all over the surface. The stochastic component represents the deviations between the irradiance according to the deterministic component and the measured irradiance.
3 Second order statistical parameters Feature autocorrelation function Definition Symbolically ∫∫n n Rnn ( a , b ) = E{n ( x , y ) n ( x + a , y + b )} 1 2 p n ,n ( n1 , n 2 ; a , b )dn1 dn 2 n1 n2 autocovariance function ∫∫(n 1 − µ n )( n 2 − µ n ) p n ,n ( n1 , n 2 ; a, b)dn1 dn 2 C ( a, b ) = R ( a, b ) − µ 2 nn nn n n1 n2 normalised autocovariance Cnn ( a, b ) C ( 0, 0 ) nn (correlation coefficient) first order (or marginal) probability density variance ∫p n ,n ( n1 , n 2 ; a , b) dn 2 rnn ( a, b) pn ( n1 ) n2 Cnn ( 0, 0) σn 2 the separation is a = 0.
