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By Ho P.-G.P. (ed.)

ISBN-10: 9533072288

ISBN-13: 9789533072289

ISBN-10: 9535108174

ISBN-13: 9789535108177

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May, 2010
(http://home. cc. umanitoba. ca/~altemey/)

Additional resources for Advances in image segmentation

Example text

31 32 Advances in Image Segmentation Figure 4. a)-(d) Images generated by Algorithm 2 with prediction windows W 1 − W 4respectively. The index Q (Wang and Bovik, 2002) is Q= (SX2 ¯Y ¯ ¯Y ¯ 4S XY X S XY 2SX SY 2X =C · M · V , 2 ¯2 ¯2 = S S · ¯2 ¯2 · + SY ) X + Y X + Y SX2 + SY2 X Y (7) ¯ is the mean of(X ) where X s s∈D , SX is the standard deviation of (X s )s∈D , and S XY is the co‐ ¯ andS ). The quantity variance between (X s )s∈D and (Y s )s∈D (and similarly for Y Y C = S XY / SX SY models the linear correlation between (X s )s∈D and(Y s )s∈D , ¯Y ¯ / (X ¯2 + Y ¯ 2)measures the similarity between the sample means (luminance) of M = 2X (X s )s∈D and(Y s )s∈D , and V = 2SX SY / (SX2 + SY2 )measures the similarity related to the contrast between the images.

Valente, M. (2011). Mammography image detec‐ tion processing for automatic micro-calcification recognition. Chilean Journal of Statis‐ tics, 2(2), 69-79. , & Vallejos, R. (2008). Codispersion coefficient for spatial and temporal series. Statistics and Probability Letters, 78(11), 1290 -1300 . , & Mardesic, T. (2004). A recursive algorithm to restore images based on robust estimation of NSHP autoregressive models. Journal of Computational and Graphical Statistics, 13(3), 674 -682 . , & Garcia-Donato, G.

Coefficient Q is defined as a function of the correlation coefficient; hence, it is able to capture only the linear association between (X s )s∈D and(Y s )s∈D It is un‐ able to account for other types of relationships between these sequences, including the spa‐ tial association in a specific direction h . Ojeda et al. 5772/50513 CQ(h ) = ρ^ (h ) · M · V , (8) where M and V are defined as in (7). The correlation coefficient and the coefficients defined in (6), (7) and (8) were computed to compare the fitted images, which were generated with a prediction window with two ele‐ ments and associated with the images shown in Figure 4(a) -(f), and the original images.

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