CI PA 2003 XIX th International Symposium, 30 September — 04 October, 2003, Antalya, Turkey 
492 
The software that was developed for this kind of 
segmentation was prepared in Visual Basic 6 environment. 
The choice of the parameters that are necessary for the 
search for the aggregation points is left to the operator. The 
choice of these parameters is of fundamental importance for 
a correct identification of the plains and it greatly depends 
on the density of the point cloud and on the size and 
complexity of the model. 
• First aggregation point 
The mean plane 
Figure 3 - An example of a plain considered not to be valid 
as seen from above. 
It is possible to obtain aggregations that are completely 
different from each other by varying these parameters. 
The software that has been developed by the authors helps 
remove the necessity of making a chance choice of the 
starting point. 
The operator can a priori choose from which point to start 
the aggregation procedure. 
2.4 Segmentation using region growing and analysis of 
the main components 
One of the models that were developed for the segmentation 
of the laser point clouds from aerial platforms is the region 
growing technique and analysis of the main components. 
This method considers the problem of grouping sets together 
of single entities that have certain properties defined by the 
partitions in the n-dimensional space of the objects. The 
algorithms of this method look for the solution using 
clustering techniques that originate from image analysis 
techniques. The function principle is based on the hypothesis 
of the knowledge of certain geometric properties, called 
describers, whose particulars one wishes to study to identify 
the partitions in a space. The solution of this problem can be 
obtained through a simple implementation: the aggregation of 
the point entity under study or region growing. The space of 
the describers is defined through the local analysis of the 
main components, which allow the local geometric properties 
of the object to be defined in an intrinsic reference system. 
The connection algorithm of the growing region begins the 
aggregation starting from a chance seed, which can be any 
entity which has not yet been aggregated; this point entity pi 
is memorised in a 0 level of a tree structure. Each point is 
identified by an indicator, which refers to the data base. 
Starting from this chance seed, the algorithm searches for the 
nearest points in an a priori defined neighbourhood in the 
space that unites the spaces of the object with the describers. 
The neighbourhoods, that is, the entities contained in such 
neighbourhoods, are aggregated and memorised in level 1 of 
the structure, as a new branch of the tree. The algorithm 
continues the aggregation starting from the entity contained 
in the last level, and so on until the terminal branches of the 
structure are obtained. The terminal branches are determined 
by the occurrence of at least one or two events. The first is 
when there are no other non aggregated entities in a 
neighbourhood; the second, when the branch contains a 
particular. The algorithm finishes the aggregation of an object 
when all the branches of the tree have reached a terminal 
point; it then starts another object starting from a new chance 
seed. 
The analysis of the main components (PCA) can be used to 
study the structure of a set of data. The PCA searches a 
vector base for a (sub) space that: 
maximises the variance of the distributions 
minimises the correlations (which is equivalent) 
The local properties of the objects in the three-dimensional 
case or in the more generic multi-dimensional case can be 
described using some describers such as: 
inertia moments and tensors 
curvature 
static moments 
The software used for the elaboration utilises two different 
types of algorithms that combine region growing techniques 
with an analysis of the main components (Roggero, 2002). 
3. EXAMPLES OF SEGMENTATION 
3.1 Identification of the main plains of the object 
The data relative to the façade of a building were treated to 
validate the efficiency and actual correct functioning of the 
proposed method. 
Figure 4 - Segmentation of a building façade. Test area of the 
Politecnico di Torino - Department of Georesources and 
Land.