Artificial targets are used in many applications to signalise locations on an ob-ject required to be measured. Shortis et al. report that in the recent decades
“...there have been a number of advances in the efficiency and effectiveness of image-based metrology systems... arguably the most important factor has been the automatic detection, recognition, identification and measurement of artificial targets used to signalise points of interest.” [Shortis et al., 2003:
202]. In photogrammetry, acquiring a variation of image views of the same target is effective because of its ability to locate homologous features to sub-pixel accuracy and to assess the dimensional stability of the camera and the object during imaging [MacDonald, 2014]. The process of object targeting in-volves the two fold task of recognition and location, in computing the subpixel locations of target images. First, recognition requires the unambiguous iden-tification of each target within an image. Second, locating the target requires precise and accurate determination of the target image centre.
Object targeting may implement natural features, based on texture content and geometry (such as natural locators: points, edges, regions), or artificial features [Rova, 2010]. The nature of the latter often make them the option of choice as they can be vastly personalised and adapted to the measurement purpose and set-up. Target artificial features can be manual (natural or retro-reflective, coded, colour, white diffuse spheres, eccentric, LEDs) or projected light (such as lasers) with relation to their form, and passive or active with relation to their illumination (for a more extensive analysis on object target-ing consult Clarke [1994], Luhmann et al. [2006]). For unambiguity, targets are optimally designed to produce sufficient contrast against their background (often designed to be the brightest or darkest object in an image), or to form specific patterns which are unlikely to be accidentally replicated by the back-ground features combined with perspective distortion [Shortis et al., 1994].
The coordinates of the targets computed photogrammetricaly produce the 3D network required [Faugeras and Hebert, 1986]. The most widely implemented are circular targets (Figure 2.23:a) due to the radial-symmetric design; as in photogrammetry, where the centre of the target is used to represent the ac-tual 3D point to be measured. Coded targets, which are formed high contrast dot with a pattern around it (Figure 2.23:b,c,d) , are also often used as they can be uniquely identified automatically by a software program from the im-ages. Figure 2.23 illustrates a selection of target samples utilised in close range measurement.
Figure 2.23: (a): Typical circular target. (b-d): Example coded targets.
Usefully, the determination of the target centre is rotation-invariant, and, also scale-invariant over a wide range of image magnifications [Luhmann et al., 2006]. In digital imaging the target centre is either calculated by centroid methods, correlation with a reference pattern, or by analytical determination of circle or ellipse centre. In the latter method, target coordinate precision val-ues are computed from the imaging geometry of the photogrammetric network in combination with the uncertainty of the target image measurements through LSE (section 2.8.1). Such information is internal to the network and hence can only provide precision information concerning the computed coordinates [Robson et al., 1993]. External checks, for example against independently measured distances between physical targets are necessary to determine the accuracy of the system (section 2.12).
2.11.1 Retro-reflective targets
A large variety of targets have been used for 3D measurement. For many ap-plications the retro-reflective target offers the best overall performance of the manually applied types [Clarke, 1994]. Special object targets have been cre-ated consisting a thin retro-reflective material whose characteristic is the high return of light in the direction of illumination. These are either covered by a black surround according to the target pattern, or are stamped in an equiva-lent form from the raw material. These retro-reflective targets consist either of a dense arrangement of small reflective balls, or an array of microprisms [Clarke, 1994]. For maximum contrast to be obtained between the targets and background, the former must be illuminated from the viewing direction of the
camera. However, normal room lighting conditions have proved sufficient for close-range photogrammetry, which is what was used in this study. This is also an important factor confirming the feasibility of the implementation of this technique in normal airport conditions.
2.11.2 Calibration reference object
In order to calibrate the interior and exterior orientation of a camera, a set of images of a reference object must be acquired. Its three imperative charac-teristics are that it is rigid, 3D of a size as big as the image space as possible (within practical limits), and has fitted targets. The total size of the object should be larger than the objects to be imaged. Also, the reference object targets may have known coordinates and a defined scale.
Figure 2.24: Two views of the ‘Manhattan’ test object, with retro-reflective targets placed on vertical rods and on its aluminium baseplate: (left) under overhead room
lights; (right) illuminated by an inbuilt camera flash, close to the lens.
The largest existing such test object fitting the ROI, the ‘Manhattan’ [Robson et al., 2014], was employed in this study is shown in Figure 2.24. It consists a rigid array of 131 3D retro-reflective targets (section 2.11.1) of ∼2.5 mm diameter with predefined coordinates, designed especially for such calibration purposes. These are affixed on the 39 anodised aluminum rods of 8 mm diam-eter and of varying lengths from 20 to 305 mm, all perpendicular to the base, or affixed on the base itself. The aluminum base is a rectangle of 550 × 550 mm and 10 mm thickness. Under flash illumination the targets are visible in the image from any viewpoint within an incidence angle limit of 50-60 degrees (Figure 2.24 right) [Robson et al., 2015]. Eight machine-readable codes are also fixed onto the baseplate to facilitate automatic orientation of the target array in image processing. As such, this object can be used to coordinate the targets on the OBT system, providing starting values.
In the cases where a test object was needed solely to calibrate the interior orientation of a camera, a secondary test object (Figure 2.25) was used, simi-lar to the Manhattan but of a smaller scale. The metal base of this object is a rectangle of 229×253 mm consisting an array of 64 3D retro-reflective targets.
These targets of ∼5mm diameter are placed at different heights, from 0 to 145
mm.
Figure 2.25: Photograph of a calibration reference object: a metal platform object with vertical rods, on top of which retro reflective targets are placed in 3D.