La perspectiva de los medios tradicionales sobre el periodismo ciudadano

An ideal camera (described in section 2.8) assumes a perfect interior orienta-tion, such that any formed image point directly corresponds to its true value.

In reality, practical cameras have optical imperfections or aberrations that mean that the actual formed imaged point is oset from the value expected from an ideal perspective projection. As the assumptions which the collinear-ity principle is based on are not achievable in practice, it is necessary to define the real camera deviation (distortions) from the ideal model [Abreu de Souza, 2009]. This is shown as a’ in Figure 2.17 which is formed at an angle i’, instead of i. In order to adjust for this, camera calibration is performed to determine how the geometry of image formation of a real camera differs from that of an ideal camera [Cooper and Robson, 1996]. This process is the camera calibra-tion (seccalibra-tion 2.9.1). By precise assumpcalibra-tions, it is meant that the image formed by a real camera will have aberrations that may degrade the image quality and alter the position of the image [Clarke and Fryer, 1998]. These can arise from the nature of the digital imaging system which can cause perturbations such as symmetric radial distortion, decentering distortion, focal plane unflatness and in plane distortions [Fraser, 2001]. In modern systems however, the key is-sues are image compression and pixel re-sampling for different image formats, which leads to the need to calibrate not just the camera but also the complete imaging system.

2.9.1 Interior camera orientation

The interior orientation parameters of a camera correspond to its intrinsic imaging properties defined by perspective projection and systematic image distortions inherent in the camera optics [Karara, 1989]. In order to deter-mine these parameter values, camera calibration is performed. The purpose of the calibration is to determine how much the geometry of image formation in a real camera differs from that of an ideal camera [Cooper and Robson, 1996].

The calibration parameters describing a camera’s internal geometric configu-ration are shown in Table 2.3. Some of these are seen in Figure 2.21, where the departure of a real camera lens from the central projection assumed by collinearity is shown. Using LSE (section 2.8.1), departures from the central projection can be modelled as systematic errors in the collinearity condition (section 2.8). Ideally, the interior orientation of a photogrammetric camera should be physically stable such that it only needs re-calibration over a long time period, which in the case of this project might amount to several months.

Parameter # Notes Principal point px

py 1 2

The point on the image plane where the optical axis of the lens intersects with the image format.

In an ideal camera the principal point location would coincide with the origin of the photo coordinate system.

Principal distance c 3 The distance between the principal point p and the perspective centre, perpendicular to the projection plane.

A real lens is subject to aberrations some of which alter the geometry of the image formed.

One to three parameters are sufficient to describe most lens distortion profiles. of individual lens elements during lens construction.

The magnitude is typically much smaller than that of radial lens distortion.

Two parameters are required to describe the magnitude and alignment.

Image deformations Corth Caf f

9 10

Orthogonality and affinity are applied to the image plane but are most often associated with optical effects.

Table 2.3: Calibration parameters describing a camera’s internal geometric configuration. [Robson, 2005].

Figure 2.21: Collinearity assumes the camera produces a perfect central projection. In real camera lenses many factors contribute towards geometric departures from this situation. These include: principal point variation, principal

distance change (due to focus and zoom), lens distortions (due to variations in diffraction), and sensor unflatness. Using least squares estimates, departures from

the central projection can be modelled as systematic error in the collinearity condition. Adapted from Robson [2005].

2.9.2 Estimation of interior camera orientation

Given a strong network of images, BA can simultaneously not only estimate the 3D coordinates in object space and pose parameters, but it can also esti-mate the parameters of interior orientation of one or more cameras.

The parameters describing the geometry of a camera (Table 2.3) can be deter-mined through a variety of different calibration methods [Fryer, 1996], such as:

Laboratory calibration: the properties of a camera system are investigated under carefully controlled conditions. For this, there are several possibilities, all of which have the common feature that the calibration is completely

sep-arately from imaging the object. The laboratory methods rely on a precise knowledge of the position and/or direction of light beam or targets, used to signalise locations on an object (section 2.11).

Self calibration: an array of special targets (section 2.11) set within the ROI is imaged from multiple viewpoints and parameters of camera interior orientation are computed using BA techniques (section 2.8.5). This is further described in section 2.9.2.1.

2.9.2.1 Self-calibration

A popular calibration method, which is used in all cases during this study, is self calibrating bundle adjustment, often simply termed self-calibration. In this method an array of targets (section 2.11) is imaged from multiple view-points and the camera interior orientation parameters are calculated using bundle adjustment (BA, section 2.8.5).

As described in LSE theory, departures from the central projection can be modelled as systematic errors in the collinearity condition. The particular calibration parameters are listed in Table 2.3, some of which are shown in Figure 2.21. The relationships of a subset of the target points require precise known dimensional constraints in providing a complete solution to these equa-tions. Here, this is provided by calibration images, in which the geometry and distances between grid points are known a priori, although in each calibration image the grid pose is unknown. In theory, the equations can then be solved, even though, in practice, the existence of measurement error suggests that this must be an approximate solution. This solution has to be determined by numerical optimization, since the equations are nonlinear. The optimisation process used here is a BA, which is one of the most widely used techniques in photogrammetry [Atkinson, 2001, Hartley and Zisserman, 2003].

A self-calibrating BA is used to simultaneously estimate the positions and orientations of each image as well as a common set of camera calibration pa-rameters (Table 2.3). Robson et al. [1993] reported that the principal point offsets together with their uncertainties can be easily determined and used as the initial approximate values for the subsequent BA process. An effi-cient technique in recovering the principal point offset and principal distance is creating a strong imaging network from a multiple camera convergent ge-ometry around a calibration reference object such as that shown in Figure 2.24. Clarke [1994] reported that in a single convergent network around the reference object, unreliable or inaccurate estimates of several or all of the inte-rior orientation parameters may occur because each camera contributes only a single image in the network. The solution to this problem is the production of multiple views of the reference object by each camera. Similar work was

carried out leading to a self-calibrating BA, and is presented in section 5.3.