Analysis of Variance in Statistical Image Processing
A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.
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addition algorithm altemative ANOVA model approach assume background and target calculated chapter column effects Computer Computer Vision conﬁdence level conformal mapping consider contrast function correlated noise correlation coefﬁcient correlation matrix corresponding deﬁned deﬁnition degrees of freedom denoted dependent noise design matrix detector diagonal edge detection F-statistic F-test feature Figure ﬁnd ﬁrst ﬁxed Gaussian Graeco-Latin square gray level homogeneous horizontal hypothesis image processing Image Segmentation Kurz Latin square layout linear model linked lists merging nested design noisy object detection obtained OOOXXXXOOO orientation orthogonal parameters partition pixels preprocessor present problem quadrants radial masks recursive reference region robust row and column row effects sample satisﬁed scanning Scheffe method segment shape test side conditions signiﬁcant speciﬁc statistically independent stochastic approximation structure sum of squares target arrays techniques template test statistic theorem threshold tile trajectory transformation vector vertical