Download e-book for iPad: 3-D Model Recognition from Stereoscopic Cues by John E. W. Mayhew, John P. Frisby

By John E. W. Mayhew, John P. Frisby

Three-D version reputation from Stereoscopic Cues ЕСТЕСТВЕННЫЕ НАУКИ, ПРОГРАММИНГ 3-D version popularity from Stereoscopic Cues (Artificial Intelligence Series)ByJohn E.W. Mayhew, John P. FrisbyPublisher:MIT Press1991 286 PagesISBN: 0262132435PDF61 MB3D version popularity from Stereoscopic Cues offers a wealthy, built-in account of labor performed inside a large-scale, multisite, Alvey-funded collaborative undertaking in desktop imaginative and prescient. It provides a number of tools for deriving floor descriptions from stereoscopic information and for matching these descriptions to third-dimensional types for the needs of item reputation, imaginative and prescient verification, self sustaining car suggestions, and robotic computer suggestions. cutting-edge imaginative and prescient platforms are defined in enough aspect to permit researchers to copy the consequences. sharingmatrix importing eighty five 1 2 three four five

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52) can be omitted, of course). 1 Global histogram-based thresholding 27 q, we only need to estimate (from the image’s histogram, as in Eqn. 32)) the “prior” probabilities P0 (q), P1 (q) and the corresponding within-class variances σ0 (q), σ1 (q). 53) K−1 1 n1 (q) · , P1 (q) ≈ p(g) = h(g) = N g=q+1 N g=q+1 q K−1 where n0 (q) = i=0 h(i), n1 (q) = i=q+1 h(i), and N = n0 (q) + n1 (q) is the total number of image pixels. Estimates for background and foreground variances σ02 (q), σ12 (q), defined in Eqns.

44 2. 9 Adaptive thresholding using Gaussian averaging (extended from Alg. 8). Parameters are the original image I, the radius r of the Gaussian kernel, variance control k, and minimum offset d. The argument to bg should be dark if the image background is darker than the structures of interest, bright if the background is brighter than the objects. The procedure MakeGaussianKernel2d(σ) creates a discrete, normalized 2D Gaussian kernel with standard deviation σ. 1: AdaptiveThresholdGauss(I, r, κ, d, bg) Input: I, intensity image of size M × N ; r, support region radius; κ, variance control parameter; d, minimum offset; bg ∈ {dark, bright}, background type.

47) (x − μj )2 1 + ln(σj2 ) − 2 ln p(Cj ) . = − · ln(2π) + 2 σj2 Since ln(2π) in Eqn. 47) is constant, it can be ignored for the classification decision, as well as the factor 12 at the front. 3. Any logarithm could be used but the natural logarithm complements the exponential function of the Gaussian. 26 2. 48) or, alternatively, to minimize (x − μj )2 + 2· ln(σj ) − ln p(Cj ) . 49) The quantity εj (x) can be viewed as a measure of the potential error involved in classifying the observed value x as being of class Cj .

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