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Creating 3D object descriptors using a genetic algorithm

In the technological world that we live in, the need for computer vision became almost as important as human vision. We are surrounded be all kinds of machines that need to have their own virtual eyes. The most developed cars have software that can analyze traffic signs in order to warn the driver about the events on the road. When we send a space rover to other planet it is important that it can analyze the ground in order to avoid obstacles that would lead to its destruction. There is still much work to be done in the field of computer vision with the view to improve the performance and speed of recognition tasks. There are many available descriptors used for 3D point cloud recognition and some of them are explained in this thesis. The aim of this work is to design descriptors that can match correctly 3D point clouds. The idea is to use artificial intelligence, in the form of a GA to obtain optimized parameters for the descriptors. For this purpose the PCL [RC11] is used, which deals with the manipulation of 3D points data. The created descriptors are explained and experiments are done to illustrate their performance. The main conclusions are that there is still much work to be done in shape recognition. The descriptor developed in this thesis that use only color information is better than the descriptors that use only shape data. Although we have achieved descriptors with good performance in this thesis, there could be a way to improve them even more. As the descriptor that use only color data is better than the shape-only descriptors, we can expect that there is a better way to represent the shape of an object. Humans can recognize better objects by shape than by color, what makes us wonder if there is a way to improve the techniques used for shape description.