Object Recognition using Grassmannian Manifolds
This is the source code for my final year project. It consists of an application that performs object recognition from a database of known objects using Grassmannian Manifolds. Each object is represented by a set of images. The data from these images is embedded into a higher dimensional space by considering it as the span of a manifold. Objects are then compared by calculating the distance between the manifolds based upon their principle angles.
- The Python Imaging Library
- Python 2.6 or greater
- NumPy or SciPy
The command line executable,
turf.py, is located in the
src folder and has
a few simple command line parameters:
usage: turf.py [-h] [-d DATABSE] [-n NEIGHBOURS] [-m METRIC] image [image ...] Turf - Object Recogniser positional arguments: image Image file to recognise optional arguments: -h, --help show this help message and exit -d DATABSE, --database DATABSE Database file. This is a YAML document specifying the training set to use -n NEIGHBOURS, --neighbours NEIGHBOURS Number of neighbours to take into account when classifying. -m METRIC, --metric METRIC Distance metric to use, on of dg, dbc, and dp For more information read the source code...