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Proceedings International Workshop on Mobile Mapping Technology
Li, Rongxing

Fei Ma and Ron Li
Department of Civil and Environmental Engineering and Geodetic Science
The Ohio State University, USA
ma.85@osu.edu, li.282@osu.edu,
KEY WORDS: feature extraction, edge detection, watershed transformation, GPS/INS, perspective geometry
This paper presents research results on feature extraction from mobile mapping imagery sequences using geometric constraints. A
comprehensive feature extraction method is developed, where first, edges are detected using a multi-scale edge detector that combines
first-order and second-order derivatives. Then multi-level thresholds are calculated to segment images. The result is used for further
watershed transformation applied to improve the segmentation result. After edge selection and line linking, for example, preliminary
road lines are detected with the help of GPS/INS data. In addition, based on perspective geometry, vertical and directional horizontal
line segments are detected and used as seeds for extracting other objects.
D ai = F(x, y) * T a (x, y) ,G 0i
Feature extraction is an area of active research in both
photogrammetry and computer vision. In mobile mapping, it is
also a critical step of object recognition. In the past, feature
extraction has been divided into several stages, namely, low level
image processing, edge detection, contour derivation, and shape
modeling. The classical method is weak in dealing with complex
scenes such as mobile mapping imagery. Later models, such as
the deformable contour model (Kass et al. 1987), treated these
problems in a general unified manner. They were used for road
extraction from land-based and aerial images (Gruen and Li
1997, Tao et al, 1998). This paper presents recent results of our
study on feature extraction from mobile mapping imagery
sequences through a multi-level approach using geometric
constraints derived from GPS/INS data.
In our study, we combine LoG (Laplacian of Gaussian) and Drog
(Derivative of Gaussian) operators into a compound edge
detector (Li et al. 1998) to take advantages of information from
second and first order derivatives. Mathematically, zero crossings
of an image F(x,y) with two scale parameters are:
G-. =F(x,y)*H^x,y) 0)
G ai =F(x,y)*H 0i (x,y) (2)
where * denotes convolution operation, H(x,y) represents LoG
function, and oj and Additionally, Drog operations with the constraint of G a are
where T^y) represents the derivative of Gaussian.
Thus, the edge detection works at two different scales. One for
extracting the raw shape of an object, called initial resolution and
the other for extracting the exact shape of the object and for
distinguishing it from other similar objects. This is implemented
by defining the initial scale edge detector of Canny (1986). The refined scale cr 2 is chosen
according to the desired details. Edges detected at the initial
resolution are intensified by edges detected at the refined
resolution. Spurious noise is avoided by intersecting the results
of the refined scale with that of the initial scale.
Figures 1(a) to (d) are two stereo pairs of mobile mapping
imagery from a sequence. Each image has 720 x 400 pixels. The
pixel size is 0.0116 mm x 0.0136 mm. Left camera focal length is
6.1288 mm and right camera focal length 6.1278mm.
(Pair I)
(a) Left
(c) Left image (Pair II)
(d) Right image (Pair II)
Figure 1. Two pairs of mobile mapping images
D a< =F(x,y)*T a Uy)-G a
(3) Figures 2(a) to (d) are edge detection results at initial scale on
four images in Figure 1.