\chapter{Conclusion}
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In this thesis, it was discussed a system for visual recognition of biological particles. Our interest is justified by the need to automatically do microscopic analysis. 
This is because manual analysis is slow, expensive, not precise and not standardized. We worked on two databases: the first one
is a collection of particles in urine, the second one is a collection of sticky tape images containing many genera of pollen.

The recognition system can be divided into two stages: detection and classification. Detection aims to find points of interest in an image, in which it exhists at least a small probability of a useful particle presence represented by those spots. Classification receives patches from the detector;
particularly, it has to skip particles that are not of interest and for the rest to determine their category.

We developed a detector based on morphological operations which gave very good results on pollen database. 
It is able to detect with probability 9/10 the 23 species of pollen in sticky tape pictures taken by light microscope. 
The percentage of false alarm is about 1000\%. Looking at the images
in our database, this performance seems very good. Even an expert has to focus his attention on nearly 10 points in each image to
establish the presence of a pollen. On the other hand, this approach is not general and reprogramming is needed to apply this detector to
another database. Furthermore, this technique is quite slow compared to other ones based on filtering, the analysis of one image takes up 10
seconds on a current 2.5GHz Pentium processor computer.

The simplest classifier with the best performance is the Bayesian classifier using mixture of Gaussians to model the training data. We
developed a new kind of features based on local jets \cite{paper:Schmid}; these features prove to capture almost all the essence of input
images because we were able to achieve an error rate of about 7.6\% on urine database and 10\% on 6 kinds of ``pure'' pollen. 
This approach is interesting because it is segmentation free, very general and able to handle very low contrast particles. \\
On the other hand, the system could be further improved using more complex technique, which extracts from points in foreground information
about texture, shape and color. \\
Moreover in a real application, the system often has to handle particles that are not of interests, for example in pollen recognition most of
the patches contain junk particles. This class should be very well classified if we admit an high number of false alarms in detection.
This is a challange to consider in the design of a good classifier, and in our experiments we took into account this class when we considered the
pollen classification task.\\

Our contribution in classification of urine particles relies on the definition of a new set of powerful features that are able to extract
information from patches of about 60x60 pixels without any segmentation. Thanks to the simplicity of our system, we are able to classify
very quickly particles; nearly 10ms are required to analyze a particle on a current 2.5GHz Pentium processor computer.\\

In pollen recognition, our work is the first research towards a feseable system for real-time pollen count in which detection and
classification are combined to recognize several kinds of pollen. \\
We used images taken with a common light microscope and our database is built using the common equipment for pollen collection, 
namely the volumetric spore trap. Unfortunately, the collection of airborne pollen grains is very slow and the available database is still very
small. This makes not very reliable our results and poor our performance. A larger database would allow a better modeling of data and
improve our performance. However, the first results showed throughout this work seem very
promising. We proved experimentally the importance to use pollen particles captured by spore trap instead of ``pure'' pollen grains, that is 
pollen grains taken directly from
flowers. Indeed, the former ones are much more variable in size, shape and texture. \\


In our opinion, urinalysis could be improved in the future if a general and segmentation free approach is applied, and if we reserve
customized operations only in a second stage for the most difficult samples.  \\
Pollen recognition needs further research but several steps have been made in the right direction. We think that in the next months a new
software could be written based on this work to detect pollen particles and make hypothesis of classification. This should be a tool to help experts, who are building their current database and speed-up the process of labelling. It is reasonable to foresee that in the next few years
a completely autonomous system for automatic and reliable recognition of pollen could be built using as input the sticky slides of 
volumetric spore trap. In the future, a new kind of collection technique able to produce input images with less debris is also possible, and
will certainly improve the performance of the visual recognition system. This will yield to a real-time and low cost measurement instrument 
for pollen count which will definitely make easier and better the life of many allergic people.


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