Mars Terrain Image Classification Using Cartesian Genetic Programming #isairas 2012

Jürgen ’Juxi’ Leitner
S. Harding, A. Förster, J. Schmidhuber

istituto dalle molle di studi sull’intelligenza artiﬁciale
università della svizzera italiana
idsia / usi / supsi

mars terrain image
classification using CGP
#iSAIRAS 2012

Wednesday, September 5, 2012

thanks to G. Metta and IIT for this picture

visual
perception


detecting
objects

learning
approach


INP INP INP + dilate min avg

cartesian
genetic
programming

Image credit: NASA

space
applications

cgp
approach

INP INP INP + dilate min avg

()*+,-*"
.-**/+,-*"%"
.-**/+,-*"$"
!"
"#$"
"#%"
cartesian
genetic
0"1/23"*)45/1" &'!"
"""
"

programming

detect

!"#$%&$'()*'+,%-$&+*,.%
•  /+0%)1#2$3%"4%)"-$.%5%677%
•  /1&+8")%3+&$%5%679%
•  :1)'8").%5%;<7%%
•  =)>1&%'"1)&%5%?@%%A1&>1&%'"1)&5%6%
•  =.,+)-%#"-$,%4"3%$B",18")%
–  C<%D"3%."E%*.,+)-.%
–  FB",18")+3G%.&3+&$HG%D6I<E%*)%$+'(%*.,+)-%
•  J$+K%.$,$'8")%>3$..13$%+>>,*$-%4"3%$0$'18")%
.>$$-%

technical
details

detecting
rocks

icImage* RockDetector::runFilter() {
! icImage* node0 = InputImages[6]->gauss(3);
! icImage* node1 = node0->sqrt();
! icImage* node9 = InputImages[5];
! icImage* node12 = node9->unsharpen(13);
! icImage* node15 = node1->mulc(7.00936886295676);
! icImage* node24 = node15->SmoothBilateral(9);
! icImage* node31 = node24->Normalize();
! icImage* node33 = node12->mulc(4.03286868333817);
! icImage* node35 = node33->add(node31);
! icImage* node99 = node35->SmoothBilateral(11);
//cleanup
...
// return
! return node99->threshold(177.2417f);
}

detecting
rocks

detecting
speciﬁc
rocks

detectingspeciﬁcrocks

classifying
martian
terrain

martian
terrain classification
I.Halatci,K.Iagnemma,etal.Astudyofvisualand tactile terrain classification and classifier fusion for planetary exploration rovers. Robotica, 26(6):767– 779, 2008.
C. Shang, D. Barnes, and Q. Shen. Facilitating effi- cient mars terrain image classification with fuzzy- rough feature selection. International Journal of Hybrid Intelligent Systems, 8(1):3–13, 2011.
C. Shang and D. Barnes. Classification of mars mcmurdo panorama images using machine learning techniques. Acta Futura, 5:29–38, 2012.


Shang et al. CGP-IP

martian
terrain classification
I.Halatci,K.Iagnemma,etal.Astudyofvisualand tactile terrain classification and classifier fusion for planetary exploration rovers. Robotica, 26(6):767– 779, 2008.
C. Shang, D. Barnes, and Q. Shen. Facilitating effi- cient mars terrain image classification with fuzzy- rough feature selection. International Journal of Hybrid Intelligent Systems, 8(1):3–13, 2011.
C. Shang and D. Barnes. Classification of mars mcmurdo panorama images using machine learning techniques. Acta Futura, 5:29–38, 2012.


collaboration

if you have (labelled) data
please contact us
we are not martian terrain
specialists :)
juxi@idsia.ch http://Juxi.net/projects


conclusions
combining cgp with opencv creates possibilities

output: executable, human-readable code
for detection and identiﬁcation
impressive performance (and robustness)


thanks
for listening
juxi@idsia.ch http://Juxi.net/projects

further references
Vincent Graziano, Tobias Glasmachers, Tom Schaul, Leo Pape, Giuseppe Cuccu,Jürgen Leitner and Jürgen
Schmidhuber. Artiﬁcial Curiosity for Autonomous Space Exploration. Acta Futura, 4, pp.41-52, 2011.

M. Frank, J. Leitner, M. Stollenga, S. Harding, A. Förster, and J. Schmidhuber. The modular behavioral environment
for humanoids and other robots (MoBeE). In Proceedings of the International Conference on Informatics in Control,
Automation and Robotics (ICINCO), 2012.

S. Harding, V. Graziano, J. Leitner, J. Schmidhuber. MT-CGP: Mixed Type Cartesian Genetic Programming. In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). Philadelphia, USA. July 2012.

Leitner, J., Harding, S., Förster, A., and Schmidhuber, J.. Mars Terrain Classiﬁcation using Cartesian Genetic
Programming. In the Proceedings of the International Symposium on AI and Robotics for Space (I-SAIRAS). 2012.

S. Harding, J. Leitner, and J. Schmidhuber. Cartesian genetic programming for image processing. Book Chapter in
Genetic Programming Theory and Practice X. Springer, 2012. (in print)

Leitner, J., Harding, S., Frank, M., Förster, A., and Schmidhuber, J. Towards Spatial Perception: Learning to Locate
Objects From Vision. In Proceedings of the Postgraduate Conference on Robotics and Development of Cognition
RobotDoc, 2012.

J. Leitner, S. Harding, M. Frank, A. Förster, and J. Schmidhuber. Transferring spatial perception between robots
operating in a shared workspace. In Intelligent Robots and Systems, 2012. accepted.


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