Insects exhibit robust terrestrial locomotion while relying on a relatively small and simple controller, the nervous system. This makes it an ideal source of bio-inspiration for robust legged walking robots. The main objective of this project is to develop an algorithm for extracting the leg positions of tethered walking flies in high-resolution movies. Ultimately, the goal is to perform a quantitative analysis of their behavior to link with simultaneously acquired neuroimaging data. This new knowledge can be applied toward inspiring more efficient and robust robotic control algorithms. The task of the student will be to design and implement a suitable algorithm for segmenting fly legs in the images of interest. This will involve searching the literature for mathematical models of leg joints in insects and investigating whether these can be used to constrain active contours for better fitting results. The segmentation method will then be incorporated into a complete tracking framework for processing video sequences. Finally, biologically relevant data will be extracted. The project is at the interface between engineering, image analysis, and neurobiology. It will primarily be supervised at the Biomedical Imaging Group, in close interaction with the Neuroengineering Laboratory (EPFL).