In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Voi. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
(a) (b) (c) (d) (e) 
Figure 4. Image processing stages: (a) Source image; (b) Retinex 
transformation result; (c) result of retinex and contrast adjustment 
stages; (d) overall image preprocessing result; (e) Texton map 
After Retinex correction mean value of luminance becomes 
equal to one. Before scaling L-component back to nonnal, 
contrast adjustment is achieved by squaring it. Then, L- 
channel is scaled back to nonnal (Figure 43(c)). This 
operation helps to make detection of road defects easier even 
in low-contrast images. 
Colour correction uses conventional grey world algorithm. In 
Lab colour space it consists of the shift of colour components 
so as to make mean value of these components be equal to 
zero, instead of scaling colour components in RGB space. In 
the final stage bilateral filtration is used to smooth image 
without loss of important details (Figure 4(d)). 
Image segmentation 
The hierarchical structures is a powerful tool to analyze data 
in many applications. Several basic approaches to 
construction of such multi-level image structure exist. The 
first approach involves recursive segmentation. An image is 
segmented in a large scale, and then segments are 
independently split into pieces. Another approach involves 
successive segmentation of an image at several scales. But in 
this case large segments not necessarily represent 
combinations of smaller ones; this fact limits the scope of 
application of this method for segmentation. 
In this work we used a method based on determination of 
strength of the boundaries between segments by means of the 
analysis of saddle points between density modes and merging 
segments that are weakly separated. For segmentation of the 
image in our work the hierarchical version of algorithm of 
mean shift, proposed in (Paris, 2007) is used. 
This algorithm provides fast hierarchical segmentation on the 
basis of idea of the saddle point analysis. Results of this 
hierarchical segmentation are shown in Figure 5, where 
borders of segments at different levels of hierarchy are shown 
in white. 
Features calculation 
A number of various features are used for classification of 
segments. We use colour statistics, such as mean values of 
CIE Lab components and mean values of RGB components, 
colour variance, Lab components’ percentiles. 
To account for shape information we calculate coordinate 
statistics, such as mass centre, coordinate variance, 
elongation, orientation, area of the segment. Usage of 
information about neighbourhood of the segment is also very 
informative for road defects detection. Accordingly distance 
between mean values of colour components inside segment 
(a) (b) (c) (d) (e) 
Figure 5.Cascade classification stages, (a) - Input image, (b) - 
ground truth image, (c) - 1 st cascade layer result, (d) - 2nd 
cascade layer result, (e) - overall algorithm result. 
and inside its neighbourhood are also included in the list of 
features. 
Texton histograms are also used in our system (Leung 1999). 
These features are proven to be highly effective in 
recognition task and are used nowadays in many detection 
and recognition systems (Criminisi, 2006). 
Previously created filter bank is applied to the image; filter 
output vectors for each pixel are associated with the nearest 
texton vectors from previously trained universal texton 
dictionary. Then histogram of textons over the segment is 
used as feature for classification task. Figure 4(e) illustrates a 
resulting texton map, which is an image, where pixels are 
labeled accordingly to corresponding textons. 
4. ONLINE STAGE OF MAPPING PROCESS 
At online stage automatic detection algorithm is applied to 
parts of rectified road image. User examines results of 
automatic detection on one image part and corrects detection 
errors if needed. Then automatic detection is adapted to new 
data. After that user goes on to the next part of the road and 
again analyses and corrects results of automatic detection. 
Accordingly automatic detector is continuously tuned in 
order to capture specifics of particular road. 
Our system provides various facilities for making process of 
error correction easier for the user. The GUI contains a 
control which lets user change segmentation level. Operator 
is able to mark ground truth in a less detailed level and then 
specify it in a more detailed one. It makes user work more 
efficient. 
Another facility allows controlling tradeoff between detection 
rate and false positive rate individually for lane marking and 
road defects. For example, user can increase detection rate of 
road defects detection (thus increasing false positive rate) by 
moving a slider. The change in detection rate is performed by 
changing a threshold on classifier output for road defects on 
the last cascade layer. This feature helps to significantly 
reduce amount of manual work in the beginning of online 
stage, when classifiers show instable performance. 
5. LANE MARKING AND PAVEMENT DEFECTS 
DETECTION ALGORITHM 
Our approach is based on cascade classifiers. The idea of 
cascades is derived from (Viola, 2002). General workflow of 
cascades is the following. There is ordered set of classifiers, 
where every subsequent classifier is more "complex" than the 
preceding one ("complexity" of the classifiers is defined 
depending on specifics of data or application). Input data