Shape recognition using squared eigen-functions of the Schrödinger operator

Shape recognition using squared eigen-functions of the Schrödinger operator

Internship Description

A new adaptive signal/image reconstruction, analysis and denoising method has been recently developed in our group where the signal is decomposed into signal dependent functions. These functions are the L2-normalized squared eigenfunctions associated to the discrete spectrum of a Schrödinger operator, the potential of which considered to be the signal/image. These signal dependent functions provide a good approximation of the signal and exhibit interesting localization properties, and they supply new parameters that can be used to extract relevant features of signal variations. They also constitute an efficient analysis tool as the information about the signal is continuously reflected on these localized functions.
In this project, the student will study the potential use of this algorithm to shape recognition where the new spectral data provided by the method will be combined with data mining approaches with a potential application to red sea marine creature recognition​​


A shape recognition algorithm will be provided with Matlab or C++ implementation.

-          A paper will be submitted 

Faculty Name

Taous Meriem Laleg

Field of Study

Electrical engineering/ Applied Mathematics/Signal processing/Data mining