Thanks to the latest improvements in machine and deep learning algorithm, intelligent imaged recognition systems have reached a new step in their performance level. However, these technologies have reached their limitation as they greatly rely on the amount of available source data to feed the algorithm and “teach it the right solution”. Synthetic images could appear as good way to tackle this scarcity of real data as long as their realism can be guaranteed.
A recent study made by IBM with OKTAL-SE simulation tools shows clearly the added value of the simulated data in the efficiency of image recognition training process (here).
With the synthetic environment approach, the user has a complete control of the scene parameters so he can define the appropriate sampling of each of them to cover a wide range of situation.
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