Supervisor: Sebastian Zambanini
Learning local image descriptors by means of deep convolutional neural nets [1,2] has recently shown to produce stronger features than traditional hand-crafted ones such as SIFT . However, these nets have been trained and evaluated on general scenarios of (wide-basline) object matching. For the DeVisOr project, matching historical aerial images with modern satellite images is of interest. In this scenario, different feature transformations have be learned due to the substantially different kinds of variations in image content.
The goal of this work is to apply modern feature learning techniques to the problem of matching historical aerial images with present-day satellite images. The work also includes the preparation of training data, for which already registered image pairs are available. Access to powerful deep learning servers will be granted.
Literature Review – getting to know the algorithms
Written Report/Thesis and final presentation