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4. How to process a vectorial image 
The amount of data produced by the Colour &3D camera is 4 times larger than that of a normal b/w camera. 
Industrial surface inspection, which always fights with the amount of data to be processed per unit of time can 
hardly tolerate an increase in processing time by a factor of 4. Is there a fast scheme of processing available? 
We propose to expand our Colour Brain concept to this new camera. Instead of processing a stored "vectorial" 
image,i.e. an image with pixels carrying a feature vector, we transform that image in video-rate into a class 
label image by using a trainable LUT pointed at by the feature vector ( Fig.5 ). 
  
  
  
  
  
  
  
  
  
40 Mbyte/sec 
8 Bit 5 Mbyte/sec 
rm > 
A E p» class label image 
V(x) : 
Colour&3D 
trainable LUT classifier 
Fig. 5 Data reduction by pixel-wise classification of the vectorial "Colour&3D" image with a trainable 
look-up table. The LUT contains the feature vector clusters of the different objects in a scene to be 
segmented and are set-up under supervised training. 
First , the LUT has to be trained to recognize given objects characterized by a given distribution of colours and 
heights. Imagine as a practical application the sorting of plastic bottles by colour and size of diameter .We 
present to the system a collection of typical samples representing class #1 ( clear white , thick) and class #2 
( green, thin ) . The RAM LUT is set to zero before starting the training and is used as an auto-increment 
4-dimensional histogram processor. Every pixel of the training sample points to a location of the RAM which is 
incremented by 1. At the end of the training for class #1, the RAM contains a cluster of entries, each representing 
the frequency of occurence of a feature vector from the pixels of the class #1 object. We binarize that 4- 
dimensional histogramm with a threshold to cancel out very rare occurences due to noise. All the memory cells 
having a frequency higher then the threshold are marked with a class code ,.#1%, the label of that class. We have 
shown in / 1/ that it is a good idea to fill holes in the cluster and to smooth it’s shape prior to thresholding. 
The same learning procedure is repeated for class #2 objects .The LUT now contains two clusters labelled as #1 
and #2. 
After the learning has been performed for all classes, the labelled RAM is turned into a LUT and ready for 
pixel-wise classification. By coding the class labels with an 1-out of- n code, we obtain at the output of the LUT 
a stack of binary class label images ( f.i. we can classify into the 4 different classes object#1, object#2, learned 
background, unknown objects with just 4 bits). This stack of 4 binary images can be furher analyzed by blob 
analysis procedures to build regions etc. Coding into a non-redundant 4 bit code such as the natural dual-code 
would allow a classifier to be built which can be trained for the recognition of 24-16 different classes 
simultanously. 
This pixel-wise classification is executed in real-time by a quite simple LUT hardware. The produced stack of 
M binary images is again readily processed by simple hardware such as run-length coder and blob forming 
processors or by software-based binary image processing algorithms. 
IAPRS, Vol. 30, Part 5W1, ISPRS Intercommission Workshop “From Pixels to Sequences”, Zurich, March 22-24 1995