- author = "J. Leitner and S. Harding and A. Forster and J. Schmidhuber",
- title = "Mars Terrain Image Classification using Cartesian Genetic Programming",
- booktitle = "11th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)",
- year = "2012",
- address = "Turin, Italy",
- month = "4-6 " # sep,
- organisation = "European Space Agency",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
-
URL = "
http://juxi.net/papers/isairas2012leitner.pdf",
- size = "8 pages",
-
abstract = "Automatically classifying terrain such as rocks, sand
and gravel from images is a challenging machine vision
problem. In addition to human designed approaches, a
great deal of progress has been made using machine
learning techniques to perform classification from
images. In this work, we demonstrate the first known
use of Cartesian Genetic Programming (CGP) to this
problem.
Our CGP for Image Processing (CGP-IP) system quickly learns classifiers and detectors for certain terrain types. The learnt program outperforms currently used techniques for classification tasks performed on a panorama image collected by the Mars Exploration Rover Spirit.",
- date-added = "2012-05-28 16:31:09 +0200",
- date-modified = "2012-05-28 16:31:09 +0200",
-
notes = "fitness = MCC, classify rocks, sand and gravel from
Mars.
Broken July 2020 http://robotics.estec.esa.int/i-SAIRAS/?q=node/6
http://robotics.estec.esa.int/i-SAIRAS/isairas2012/i-SAIRAS2012.pdf",
