Scalable wavelet on biological images
Autumn 2014
Master Diploma
Project: 00285
Wavelet representations are ideally suited to local feature detection in images. For example, steerable wavelets (and steerable filters) have gain interest for efficient and accurate rotational-invariant object detection. In our laboratory, we have designed efficient tools for scaling-invariant feature detection based on a new families of scalable. This is ideally relevant for the biological applications.
The goal of this project is to implement this tool as an ImageJ plugin with an user interface. The method will be validated on biological images, both for detection and feature enhancement.
- Supervisors
- Zsuzsanna Puspöki, zsuzsanna.puspoki@epfl.ch, 021 693 51 57, BM 4.139
- Michael Unser, michael.unser@epfl.ch, 021 693 51 75, BM 4.136
- John-Paul Ward, Daniel Sage