By Christian Wöhler
This critical textual content introduces the principles of third-dimensional machine imaginative and prescient and describes fresh contributions to the sphere. totally revised and up to date, this much-anticipated new version studies a variety of triangulation-based equipment, together with linear and package deal adjustment established techniques to scene reconstruction and digital camera calibration, stereo imaginative and prescient, element cloud segmentation, and pose estimation of inflexible, articulated, and versatile items. additionally lined are intensity-based thoughts that assessment the pixel gray values within the photograph to deduce three-d scene constitution, and element unfold functionality established techniques that make the most the impression of the optical process. The textual content indicates how equipment which combine those options may be able to bring up reconstruction accuracy and robustness, describing functions in business caliber inspection and metrology, human-robot interplay, and distant sensing.
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Extra resources for 3D Computer Vision: Efficient Methods and Applications
UN ⎥ L16 ⎝Q ⎦ N vN QN −uN zN QN −vN zN QN ⎞ ⎟ ⎟ ⎟ ⎟. 33) where M is a rectangular matrix of size 2N × 16, B a column vector of length 2N , and L a column vector of length 16 containing the parameters L1 . . L16 . 33) amounts to eight if all 16 parameters are desired to be recovered. In the absence of lens distortions, only 11 parameters need to be recovered based on at least six control points. It is of course favourable to utilise more than the minimum necessary number of control points since the measured pixel coordinates ui and vi are not error-free.
Fig. 7b). 1. Edge Circle Filter The filter is implemented as a mark-and-sweep algorithm. In the first run, all corner candidates are marked which are part of at least one circle of length 4. The second run eliminates all candidates that are not marked. 83) is marked. e. (s, t1 , d) ∈ E and (s, t2 , opposite(d)) ∈ E, the lengths l1 = S xs − S xt1 and l2 = S xs − S xt2 are determined. 84) is not fulfilled, the lengths of the edges are assumed to be so strongly different that this difference cannot be explained as a result of lens distortion or a slanted calibration rig.
57) reduces to the simplified form [A1 | 0] = [I | 0]H , from which it can be readily inferred that B = A1 and t = 0. To prevent the matrix H from becoming singular, it is required that its element H44 = k = 0. A favourable choice is to set H44 = k = 1, which yields H= A1 vT 0 . 60) and hence vT = −pT A with the upper triangular matrix A ≡ A1 denoting the intrinsic calibration of the first camera (Hartley and Zisserman, 2003). 59) then becomes H= A −pT A 0 . 4 Geometric Calibration of Single and Multiple Cameras 27 The metric reconstruction then consists of a determination of the three components of p and the five independent matrix elements of A (Hartley and Zisserman, 2003).
3D Computer Vision: Efficient Methods and Applications by Christian Wöhler