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 7A: Automatic Object Extraction and Recognition]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: DATABASE GUIDED VERIFICATION AND UPDATING OF TRANSPORTATION OBJECTS WITH VERTICAL LINE FEATURES FROM MOBILE MAPPING IMAGE SEQUENCES. C. Tao.

Document type:: Monograph

Structure type:: Chapter

7A-4-2 Transportation Object Imaging and GPS/INS Georeferencing Figure 2. Overview of the approach • Back projection of objects from a database onto images • Verification of the existence of objects • Multinocular line reconstruction for accurate object positioning The mobile mapping image sequences collected have been georeferenced using GPS/INS positioning and photogrammetric CCD calibration techniques. Consequently, the orientation and position parameters for each camera exposure station are determined with respect to a global coordinate system. For more detailed information on image georeferencing methods, one can refer to Schwarz and El-Sheimy (1996) and El-Sheimy (1996). 3. VERIFICATION OF THE EXISTENCE OF OBJECTS The mounting of the stereo cameras in the VISAT system follows the way that the baseline is constrained approximately to be parallel to the ground plane and the camera roll angle is configured to be very small. As a result, the projections of 3-D line features of the objects in images are also approximately vertical. 3.1 Detection of Vertical Edges J Edge magnitude and edge direction are two main components of an image edge element. We firstly recognize from experiments that information related to edge direction is valuable since the edge magnitude is easily corrupted by noise and physical factors, such as shadows. Therefore, the computed edge direction is used as the main evidence for inference. In the following, a three-step algorithm to determine vertical edges is described: (a) determination of the existing region of possible edges; (b) determination of the existing region of thinned edges; and (c) determination of the existing region of vertically oriented edges. 3.1.1 Determination of the Existing Region of Possible Edges We start with convolving an input image using the six directional masks (Tao, 1997). After convolving the masks with the original image, the magnitude of the convolved output and the direction of the mask giving the highest output at each pixel are recorded as edge data. A very small threshold is set in order that all possible edges are detected in the edge existing region. The direction value is encoded as 0 - 5 corresponding to the six directions previously defined. An example of the obtained edge existing region is shown in Figure 3b. 3.1.2 Determination of the Existing Region of Thinned Edges The thinning algorithm is designed as follows, where the obtained direction values of edges are used to constrain the thinning procedure: an edge element exists if the following two conditions are satisfied The transportation objects to be examined are firstly queried from an existing database. The position of the desired object is projected onto the corresponding images using photogrammetric collinear equations. A global coordinate system, i.e., UTM coordinate system, is used during the projection. The normal speed of the mobile mapping platform (van) is around 50-60 km per hour and the image capture rate is about 0.4 second. As a result, an object is usually visible in 3-4 image stereo pairs. Due to the use of the prior information of the object position, the approximate position of the object in the images is determined. Thus, the processing window of the object in the images can be predicated (shown in Figure 2). It is recognized that the use of this prior information greatly simplifies the procedure of object detection, and moreover, makes the processing results much reliable. The module of verification of the existence of the objects is to identify whether or not the object exists (or misses). It involves a number of processing steps: detection of vertical edges, formation of line segments and feature correspondence of line segments. Once the existence of the object is verified, the module of multinocular line reconstruction is executed to determine the accurate position of the object, and then update the position information of the object in its database. • the edge directions of the two horizontally neighboring pixels are within one unit (45°) of that of the center pixel; and • the output edge magnitude at the pixel is larger than the edge magnitudes of its two neighbors in a direction normal to the direction of this edge. If the above two conditions are met, the two neighboring pixels are disqualified from being candidates for edges. An example of edge thinning is given in Figure 3c. 3.1.3 Determination of the Existing Region of Vertically Oriented Edges A further refinement is conducted using the accurate values of edge direction. In this step, we use the Sobel gradient operator to compute the direction values of the existing edges. Since a large amount of the undesired edges have been screened out through the previous two steps, the computation of edge gradients is only performed on a small set of edges, and the savings on the computation time are significant. After the computation of gradients, a rigorous tolerance of direction value, 15°, is applied to filter out all unqualified edges. The processing result after this step is illustrated in Figure 3d.

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