Proceedings International Workshop on Mobile Mapping Technology

li, rongxing

Bibliographic data

Monograph

Persistent identifier:: 856671290

Author:: Li, Rongxing

Title:: Proceedings International Workshop on Mobile Mapping Technology

Sub title:: April 21 - 23, 1999, Bangkok, Thailand

Scope:: 1 Online-Ressource (Getr. Zählung [ca. 400 Seiten])

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856671290

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Session 2: Mobile Mapping (2)]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: TOWARDS AUTOMATED PROCESSING OF MOBILE MAPPING IMAGE SEQUENCES. C. Tao, M. A. Chapman, and N. El-Sheimy, B. Chaplin.

Document type:: Monograph

Structure type:: Chapter

' "• : ' ' photogrammetric applications, such as quality control, camera calibration, sensor navigation and object reconstruction. Reviews of references on algorithms for estimation of motion/structure parameters from image sequences have been provided by Aggarwal and Nandhakumar (1988), and Huang and Netravali (1994). 3.2 Short Range vs. Long Range (Continuous vs. Discrete) Motion Analysis Generally, there are two complementary classifications of schemes to compute visual motion. The first classifies according to the spatio-temporal range over which methods are applicable, analogous to the human visual system: (1) short range motion (continuous) process and long range motion (discrete) process. The second classification distinguishes between the fundamentally different processes involved: (2) optical flow and correspondence. In fact, the optical flow scheme, which uses image gradients to derive image motion, is intrinsically restricted t short range, while correspondence or similarity matching schemes can be of short range or long range. In terms of short range motion analysis, images are taken at video rate. Thus the emphasis is generally placed on the estimation of the optical flow field between two successive frames, or on the direct use of the spatio-temporal derivatives of the image brightness. These observations must also be combined with a measure of the camera velocity (instead of camera displacement) to determine the 3-D structure of objects. In long range motion analysis, images are acquired at larger time intervals, and a large camera displacement is observed. Since the image motion of the features is “large” compared to the temporal sampling rate, the eye has to solve the correspondence problem, i.e., it has to establish which feature at one time instant corresponds to which feature at the next time instant. Therefore, in long range motion analysis, a set of relatively sparse, distinguishable two-dimensional features, such as points, straight lines, curved lines, corners and regions, in the successive images is firstly extracted. Secondly, feature correspondences are established between consecutive features, and finally, the 3-D structure of the object and its relative motion with respect to the camera can be determined based on the motion of these features. It is worth mentioning that most of the research for long range motion analysis has concentrated on determining motion estimation and feature correspondences over a short image sequence (i.e., two to three images). In general, if the scene has many easily identifiable feature points or lines, the discrete approach based on feature correspondence is suitable. If the surfaces in the scene are smooth and have no texture, then the continuous approach based on intensity derivatives is better. However, robust and accurate computation of feature correspondence and optical flow still remains a difficult problem. The optical flow field is often corrupted by image noise or occlusion, leading to generally poor and unstable results in the 3-D reconstruction. Feature correspondence also easily fails in areas where either the distortion is large, or the occlusion occurs. Hybrid approaches combining both feature correspondence and optical flow would be a way to alleviate the above problems (Baker et al., 1994; Hanna and Okamoto, 1993; and Navab and Zhang, 1994). The research showed that optical flow field based approach is not suitable for the VISAT images, since the image capture . si:! interval is about 0.4 second and the camera movement between the imaging intervals is large and of the order of 6-10 meters. Intuitively, our research falls in the category of long range motion analysis. However, compared to the processing of monocular image sequences commonly addressed in the literature, we are dealing with binocular image sequences. Such redundant image information allows us to develop more robust algorithms for the processing of image sequences. In this research, feature correspondence and image matching techniques are mainly used in the proposed methods. There are a number of good references available with reviews of techniques for feature correspondence and image matching (Agouris, 1992; Baltsavias, 1991; Barnard and Fischler, 1982; Dhond and Aggarwal, 1989; Forstner, 1993; Gruen, 1994; Jones, 1997, and Lemmens, 1988 and Mass, 1996). 3.3 Visual Motion Analysis with Known Ego-Motion Vision analysis with known ego-motion refers to motion analysis under known dynamics of the camera (observer). In fact, known ego-motion analysis forms the basis of an active vision system. Under the condition of known ego-motion, the 3- D reconstruction problem can be solved more efficiently. This fact has motivated some investigations (Aloimonos et al., 1988; Bajcsy, 1988). On the other hand, accurate geometric constraints, such as the epipolar line constraint, are also available, resulting in a more robust realization of feature correspondences. In the VISAT mobile mapping system, the kinematic trajectory of the vehicle can be determined with a high accuracy of 5-15 cm, and the camera dynamics can be examined rigorously by using GPS/INS georeferencing technique (Schwarz and El- Sheimy, 1996). As a result, visual analysis can be conducted under the constraint of known ego-motion. It will be seen that this constraint is very valuable for automating and optimizing a reliable procedure for object measurement and feature extraction. 3.4 Active Vision A very important advance in the theoretical framework of computer vision is the concept of active vision, proposed by Aloimonos et al. (1988). Active vision represents a behaviorism school, which is directly opposite to Marr’s theory of vision, a recovery school (Marr, 1982). There is a noncontroversial observation that vision is an underconstrained problem. Thus the main goal of vision work is to find and develop constraints. However, rather than focusing on narrow sources of constraints, mostly oversimplified constraints such as smoothness constraints widely used in the recovery school, it is argued that one must exploit constraints from all possible sources and incorporate them systematically. The basic idea of active vision is the introduction of a new source of constraints arising from the internal architecture of the system itself and the iteration of its components, such as observer-based constraints, e.g., the sensor and/or the computer (Jolion, 1994). Under the constraint that the active observer moves with known motion, a unique solution is available, resulting in a well-posed formulation of the problem. Moreover, the knowledge of these viewpoints of the active observer increases the robustness to noise. The known motion of the observer can be determined by the use of advanced navigation technology. In this context, an imaging 2-5-3 ■ 3É6

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