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1 250 km b) Inclusion of an HRDP in the ISDN hypothetical reference connection based upon ITU-T Recommendation G.821 LE: T: Local exchange T reference point

Substitute the array formula in range A5:A20 for the references to that range. The resulting array formula is:

where 2 = 2 x 2 + 2 y2 is the Laplacian operator, G ( x, y) the twodimensional Gaussian function, and the convolution operator In the discrete case, both operations are performed with nite pixel masks, usually rounded off to integer values; the separability property of the Gaussian kernel G ( x, y) = G ( x ) G ( y) for any allows very ef cient implementation by means of two monodimensional lters, respectively, along the rows and columns of the image Unlike the Canny detector, this method provides connected and closed image contours located roughly on the boundaries of image structures at different scales, obtained by varying the size of the LoG kernel In particular, at higher scales all small-sized blobs are increasingly suppressed (Fig 434).

Some Excel functions belong to a special class of functions called volatile. Excel recalculates a volatile function whenever it recalculates the workbook even if the formula that contains the function is not involved in the recalculation. The RAND function represents an example of a volatile function because it generates a new random number every time Excel calculates the worksheet. Other volatile functions include: AREAS CELL COLUMNS INDEX INDIRECT NOW OFFSET ROWS TODAY

This formula returns the character position of the last space in the string. You may wonder how all of these formulas can possibly be combined into a single formula. Keep reading for the answer.

On the other hand, because of the Gaussian smoothing, at coarse scales the object contours are also poorly localized; in particular, high-curvature edges such as corners are rounded off Therefore, this method provides a multiscale approach to edge detection and object tracking as well: By proceeding from coarse to ne resolution, we can obtain more robustness and larger convergence regions for both image-space and state-space search algorithms The standard output of both detectors is a scalar binary map where edge pixels are set to 1 However, the direction and magnitude of an intensity edge is also valuable information that should be retained for object tracking, because if the pose hypothesis is correct, the gradient onto a sharp edge should possess normal orientation to the projected contour as well as signi cant magnitude In fact, many small blobs on the low-scale edge detection (Fig 4.

AP9.2-7 TABLE AP9.3-1

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34, left) can be removed simply by thresholding their magnitude After image edges have been detected, the next step is to match them with the relevant model features; rst we consider the case of individual model points sampled along the visible silhouette In our abstraction, pixel-level data association with the edge map is carried out using the Chamfer distance transform [64]; the map on the right of Fig 435 represents, for each pixel, the distance to the closest edge in the Canny map, which is model-free and therefore part of the preprocessing step..

// returns the standard deviation sigma, de ned as the // square root of the variance. double standardDeviation() const; // returns the downside variance, de ned as // N/N-1 *sum_{i=1}^{N} // theta*x_i^{2}}{sum_{i=1}^{N} w_i}, // where theta = 0 if x > 0 and theta =1 if x <0 double downsideVariance() const; // returns the downside deviation, de ned as the // square root of the downside variance. double downsideDeviation() const; // returns the error estimate epsilon, de ned as the // square root of the ratio of the variance to the number of // samples. double errorEstimate() const; // returns the skewness, de ned as // [ \frac{N^2}{(N-1)(N-2)} \frac{\left\langle \left( // x-\langle x \rangle \right)^3 \right\rangle}{\sigma^3}. ] // The above evaluates to 0 for a Gaussian distribution. double skewness() const; // returns the excess kurtosis, de ned as // N(N+1)/(N-1)(N-2)(N-3) // \frac{\left\langle \left( x-\langle x \rangle \right)^4 // \right\rangle}{\sigma^4} - \frac{3(N-1)^2}{(N-2)(N-3)}. // The above evaluates to 0 for a Gaussian distribution. double kurtosis() const; /* returns the minimum sample value */ double min() const; /* returns the maximum sample value */ double max() const;

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