1. Will Speak
  2. Turf

Overview

HTTPS SSH

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.

Prerequisites

  • The Python Imaging Library
  • Python 2.6 or greater
  • NumPy or SciPy
  • MDP

Usage

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...