Download Accurate Visual Metrology from Single and Multiple by Antonio Criminisi PDF

By Antonio Criminisi

Accurate visible Metrology from unmarried and a number of Uncalibrated Images offers novel strategies for developing 3-dimensional types from bi-dimensional photos utilizing digital fact instruments. Antonio Criminisi develops the mathematical idea of computing international measurements from unmarried photographs, and builds up a hierarchy of novel, versatile concepts to make measurements and reconstruct third-dimensional scenes from uncalibrated photographs, paying specific recognition to the accuracy of the reconstruction.
This ebook contains examples of fascinating possible purposes (eg. Forensic technology, heritage of paintings, digital fact, Architectural and indoor measurements), provided in an easy approach, observed through images, diagrams and many labored examples to assist the reader comprehend and enforce the algorithms.

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Additional info for Accurate Visual Metrology from Single and Multiple Uncalibrated Images

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One view only constrains each image point to lie on its projection ray but, in order to obtain the depth of a three-dimensional point with respect to a reference plane (or a generic surface), a second view is in general necessary. The parallax vector seen in the second image (see Fig. 3) is, in fact, a function of the depth of the point from the reference plane. The parallax approach has been used by Cipolla in [15) and by Cipolla and Giblin in [17) as a way to estimate robustly the curvature of surfaces.

This situation is considered in Chapter 6 where the parallax effect of points off the distinguished plane is analyzed. 3 Planar homology In this section another interesting projective transformation is described: a planar homology. This is used extensively in the following chapters. 1 Camera models and perspective mappings 29 Fig. 5. Inter-image homography: the floor of a chapel viewed in both images induces a homography. Points can be mapped from one image to the other. Vertex~,V , , \qI . ~ Fig.

149 §. 15 0 15 . 065 d Fig. 4. Uncertainty in point localization. 2 using only four computation points. Ten thousand image points are randomly generated from a Gaussian distribution centred on an image test point x and then back-projected onto the world plane. The statistical covariance ellipse of the world points is computed and plotted, together with the predicted one. Three standard deviations are visualized for each uncertainty ellipse. b The areas of the first order and simulated uncertainty ellipses decrease as the number of computation points increase from four to five to ten as expected from the theory.

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