International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
discussed in section 5 and conclusions and discussions of 
future works are outlined in section 6. 
2. MULTI-SENSOR DATA FUSION 
Multi-sensor data fusion refers to, in our context, the 
techniques for the combination of datasets from the 3D point 
cloud data and the 2D image (i.e. intrinsic parameter of the 
CCD camera) to create a new dataset. The input to this process 
is 3D data from 3D laser scanner, the 2D intensity image from 
independent CCD camera, and the intrinsic camera parameters. 
These sensors are not calibrated. Detailed description of the 
coordinate systems of the 2D and the 3D sensors, data capture 
and processing is available in Forkuo and King (2004). 
2.14 3D Point Cloud 
Cyrax 2500 Laser Scanner was used to carry out the laser 
scanning to acquire a discrete representation of the object. 
More description of the laser scanner used in this experiment 
can be found in (Forkuo and King, 2004; CYRA, 2004). Figure 
| shows a screen capture of pseudo-colored 3D point cloud 
data of the test area. The 3D point cloud allows for the 
construction of a 3D surface Model of the scene. The resolution 
of the scan, which controls the number of points recorded in a 
scene and the level of detail visible in a scan (Barber and 
Bryan, 2001), is simply the smallest change in distance that the 
scanner is capable of detecting. 
T 
3 
    
  
Figure | A screen capture of Pseudo-colored 3D point cloud 
2.1.2 2D Intensity Images 
A series of images were taken at different direction and 
position (as depicted in figure 2.2) by the digital CCD camera 
(Nikon DI1x), which produces an image of at the size of 
23.7mm x 15.6mm width. These images are called real camera 
images (RCI) and one of these images is represented in figure 
2. This camera provides a digital image with the resolution of 
3008 by 1960 pixels at true color mode. 
2.2 Backprojection of Laser Scanning Data 
Multi-sensor mathematical model is a physical model that 
describes the integration of 3D laser range camera and the 
CCD camera (Forkuo and King, 2004) We use the 
photogrammetric principles of collinearity condition with no 
systematic correction parameters as the basis for the 
implementation of the transformation of 3D point cloud data to 
suitable 2D shape information. For details on the collinearity 
model and the subsequent steps in fusing the dataset, see 
Forkuo and King (2004). 
  
Figure 2 Real Camera Image (RCI) 
2.3 The Synthetic Camera Image 
This task is to represent the results of the collinearity equation 
(discrete x, y) points as image, which could be used in the 
image-to-image registration task discussed in section 3. A more 
detailed description can be found in Forkuo and King (2004). . 
By way of definition, interpolating the backprojected laser 
point (which contains irregular point spacing) into a regular 
grid at an even spacing using the intensity values generates 
what is termed “the Synthetic Camera Image" (SCI). There are 
two options related to this interpolation. First option is to 
generate the SCI by keeping the original resolution of the point 
cloud data and the compute a new pixel size. The second 
option, on the other hand, is to keep the pixel size of the real 
camera image and then compute the number of pixels or the 
resolution. In this paper, this option is used to generate the SCL 
The interpolated data was then modeled by f (x, y) = I, where 
(x, y) is the pixel position and the / , the corresponding 
intensity value which is mapped to a grayscale. Conventional 
image processing techniques such contrast stretching and 
image enhancement were then used to produce the final image 
in figure 3. It is obvious that the geometric features in the SCI 
are easier to detect than those in the laser range data. This 
image offers a major advantage to interactively (controlled by 
human operator) or automatically conjugate matching with the 
intensity images produced by digital camera. 
  
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