By Nicu Sebe, Michael S. Lew
From the foreword by means of Thomas Huang: "During the earlier decade, researchers in laptop imaginative and prescient have stumbled on that probabilistic desktop studying tools are super strong. This publication describes a few of these equipment. as well as the utmost chance framework, Bayesian Networks, and Hidden Markov versions also are used. 3 points are under pressure: beneficial properties, similarity metric, and versions. Many attention-grabbing and demanding new effects, in line with study by means of the authors and their collaborators, are offered. even supposing this publication comprises many new effects, it really is written in a mode that matches either specialists and beginners in machine vision."
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Additional resources for Robust Computer Vision: Theory and Applications (Computational Imaging and Vision Series, Volume 26)
Neither Φ(z) nor erf(·) can be expressed in terms of finite additions, subtractions, multiplications, and root extractions, and so both must be either computed numerically or otherwise approximated. The value of a for which P (x) falls within the interval [−a, a] with a given probability P is called the P confidence interval. 16) gives √ 1 z (x − µ)2 d dx = z = dx. 18) 2 2 Γ 2 √ where we used the identity Γ(1/2) = π. 1 (Central Limit Theorem) Let x1 , x2 , . . , xN be a set of N independent random variates and each xi have an arbitrary probability distribution P (x1 , x2 , .
There is no question that any observation is in any way erroneous. 130) Clearly if either of these situations holds, then there will be a tendency for the larger order statistic to be suspiciously large relative to its predecessor, and so samples generated by outlier-prone distributions will tend to contain visual outliers. Absolutely and relatively outlier-resistant distributions are those which are not absolutely and relatively outlier-prone, respectively. The effect of outliers on the analysis of a set of data depends strongly on the mechanism by which the outliers are believed to be generated.
3. 107) and the mean, variance, skewness, and kurtosis are µ σ2 γ1 γ2 = = = = Consider now the distribution of differences between two independent variates with identical exponential distributions. 3. 4. The Cauchy distribution describes the distribution of horizontal distances at which a line segment tilted at a random angle θ cuts the x-axis. 117) Cauchy Distribution The Cauchy distribution, also called the Lorentzian distribution, is a continuous distribution describing resonance behavior. It also describes the distribution of horizontal distances at which a line segment tilted at a random angle cuts the x-axis.