Corner detection is a classical task in computer vision. A variety of methods are available and routinely used, which includes the Harris corner detector and other Hessian-based approaches. The aim of this project is to develop a novel general-purpose corner detection algorithm. Corners can indeed be seen as Green functions of the partial differential operator DxDy. The originality of the approach would then be to build a detector using a differential operators formalism. Additional properties such as scale and rotation invariance could also be granted using a wavelet-based theoretical framework developed in the lab. The project contains both strong theoretical and practical aspects and can be tuned to the student’s interest. The mathematical counterpart includes the proper formulation of the corner detection problem, while the practical part mostly consists of implementing the method and evaluating its performance on synthetic and real data.