Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
The work presented in this document deals with Machine learning algorithms used in
surgical robotic problems, especially here in the Isis project. The aim of the project is
to replace manual handles for endoscopic operations by a set of motors commanded
via high-tech interface. The main aim of the thesis is to solve the problem of the
estimation of the pose of flexible surgical instrument, using only the video flux from
an endoscopic camera.
After a short introduction about the system its environment and the definition of the
pose problem, the work is divided in two chapters. Machine learning algorithms and
learning systems are used in both parts.
The first chapter deals with image processing and video tracking. Usage of colored
markers and how the learning is made to perform the best segmentation is explained.
It starts with simple linear classification to end with an Adaboost algorithm. The
learning database construction and all the challenges it raises are explained too.
Then the tracker used in the system is decomposed and its structure explained. The
results of the whole tracking system are presented at the end of the chapter.
The second chapter deals with function approximation: we build Radial Basis
Function networks in order to approximate the final position of the instrument (or its
mechanic parameters) from the data extracted by the tracking system. Learning
algorithms are used too. The learning training set is built in a laboratory environment
where the real position of the instrument can be measured.
The last chapter is a collection of improvements that could be added to the system
and opens on future perspectives about the project in general.