3 D Graphics

Accurate Visual Metrology from Single and Multiple by Antonio Criminisi

By Antonio Criminisi

Accurate visible Metrology from unmarried and a number of Uncalibrated Images offers novel strategies for developing third-dimensional types from bi-dimensional photographs utilizing digital truth instruments. Antonio Criminisi develops the mathematical thought of computing global measurements from unmarried photos, and builds up a hierarchy of novel, versatile suggestions to make measurements and reconstruct 3-dimensional scenes from uncalibrated photos, paying specific recognition to the accuracy of the reconstruction.
This publication comprises examples of fascinating potential purposes (eg. Forensic technological know-how, heritage of paintings, digital fact, Architectural and indoor measurements), awarded in an easy manner, followed through images, diagrams and lots of labored examples to assist the reader comprehend and enforce the algorithms.

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

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All the computation image- and world-points are assumed to be measured with error modelled as a bi-dimensional Gaussian noise process (see Fig. 13). Ax. and Ax. are the covariance matrices of the image computation point Xi and the world computation point Xi respectively. The two sources of error (the uncertainty on the homography and the uncertainty in image point localization) are first considered to operate separately, and finally they are merged in order to compute an unique formula embracing both cases.

Fig. 2). In this section, formulae to compute the uncertainty for planar measurements under various error situations are developed. The first order analysis is assumed sufficient. All the computation image- and world-points are assumed to be measured with error modelled as a bi-dimensional Gaussian noise process (see Fig. 13). Ax. and Ax. are the covariance matrices of the image computation point Xi and the world computation point Xi respectively. The two sources of error (the uncertainty on the homography and the uncertainty in image point localization) are first considered to operate separately, and finally they are merged in order to compute an unique formula embracing both cases.

An example. 9a shows an image captured from a cheap security-type camera which exhibited radial distortion (note how straight edges in the scene appear curved in the image). Edges have been extracted automatically by a Canny edge detector [10]. A set of edges assumed to be straight in the scene has been selected (Fig. 9b) and from those the distortion parameters computed and the image corrected accordingly (Fig. 9c). Note that now images of straight edges in the world are straight (for example, the left edge of the filing cabinet).

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