The MarsFace project, presented at PyCon APAC 2015, aims to detect pseudo-artificial structures on Mars using advanced image analysis techniques. It utilizes deep learning algorithms and significant computational resources to analyze over 19,000 satellite images, achieving high accuracy in detecting features. The presentation also discusses future applications and the integration of crowdsourcing for enhanced object recognition.

1. Hideki Tanaka PyCon APAC 2015
Marsface Project
Detecting Pseudo-artificial Structures on Mars
x

2. SciPyTalk

3. SciFiTalk

4. http://marproject.org/index2.html

5. Image: CC BY: International Space Apps Challenge Tokyo on Flickr

6. International Space Apps Challenge
(https://2015.spaceappschallenge.org/)
[ISAC]

7. @atelierhide

8. ?@atelierhide =

9. = Lens Designer@atelierhide

11. = Photographer@atelierhide

12. © Hideki Tanaka

13. © Hideki Tanaka

14. = Organizer@atelierhide

15. http://pydatatokyo.connpass.com/
[PyData.Tokyo]

16. @PyDataTokyo Organizers
@iktakahiro@madyagi
@atelierhide@kaita

17. Venue Sponsor = Denso IT Laboratory
© PyData.Tokyo

18. http://pydataokinawa.connpass.com/
[PyData.Okinawa]

20. The CFP for PyCon JP is open until July 15!
(https://pycon.jp/2015/en/)
[PyCon JP]

21. What is Marsface Project?

22. 1976

23. NASA’s Viking spacecraft discovered
the ”Face on Mars.”

24. [Face on Mars]

25. 2001

26. The ”Face on Mars” turned out
to be an optical illusion.

27. [Face on Mars]

28. However, some people think
it is evidence of life on Mars,
that NASA would rather hide.

30. [Human on Mars]

31. Marsface Detector
Marsobject Detector Pseudo-artificial
Structures
Satellite
Images

32. 19,000+ space images are available!
[NASA/JPL]

33. Computational Resources
Supported by Microsoft Azure Research Award Program
32 compute instances
10 TB of storage
1 billion storage transactions
10 shared websites/10 shared mobile services
100 million service bus messages
100 GB SQL database
2 TBs network egress/month
The estimated total market value: $40,000

34. How Marsface Detector works?

35. Related Research: [Geoface Project]
Face-like structures on Google Maps
(Kazutaka Kurihara, EC 2010)

36. Face detection with Haar-like features
[OpenCV]
OpenCV-Python bindings

38. Lots of false positives…

40. [LROC]

41. [LROC]

42. [LROC]

43. [LROC]

44. [LROC]
3.0km
“Face on Moon South Pole”

45. [Wikipedia]
http://en.wikipedia.org/wiki/Face_on_Moon_South_Pole

46. Space Souvenirs!!!

47. 131,880 JPY = 32,760 TWD = 1,064 USD
(Gold)
3D Printed Mold
[DMM1]
http://make.dmm.com/item/116196/

48. Other Discoveries…
OpenCV, LibCCV (Surf-based detector) etc…

49. Algorithms
False positives
LessMore
SophisticatedSimple
Detected Faces
Human-likeAlien-like

50. How Marsobject Detector works?

51. Deep Learning
input output
hidden × n

52. [Kaggle]

53. Accuracy: 98.5% with Deep Learning!!!
[Kaggle]

54. ImageNet Large Scale Visual Recognition Challenge 2012
Method
SuperVision
SuperVision
ISI
Team Name
Error
(5 guesses)
7 CNNs
5 CNNs
Fisher Vectors
15.3%
16.4%
26.2%
Image classification with 1000 categories

55. Convolutional Neural Networks (CNNs)
[SuperVision]
Input Convolutional Layers Fully
Connected
Output
Cat

56. Core
Language
Binding
Theano/Pylearn2
cuda-convnet
OverFeat
DeCAF
Caffe
Python
C++
Lua
Python
C++
-
Python
Python
-
Python
Pre-trained
Models
Framework
×
×
○
○
○
Deep Learning Frameworks
ImageNet

57. [Model Zoo]

58. Obj.
Tabby Cat
Tiger Cat
Egyptian Cat
Red Fox
Lynx
Scores
0.31
0.21
0.13
0.13
0.07
[Notebook]

59. Obj.
Orangutan
Chimpanzee
Car Mirror
Gorilla
Hippopotamus
Scores
0.23
0.10
0.07
0.04
0.03
[Cat & Dog]

60. [Selective Search]
Search over position,
scale, aspect ratio
Grouping parts of
image at different scales
Sliding Window Selective Search
Segmentation as Selective Search for Object Recognition

61. R-CNN
(Regions with Convolutional Neural Network Features)
Selective Search(MATLAB) + CNNs(C++)
[R-CNN]

62. “Happy Face Creator”

63. Lizard?

64. African Grey

65. Golf Ball…

66. Future Works
1. New segmentation techniques
2. Object recognition by IBM Watson
3. Crowdsourcing as a screening tool (oDesk)
4. More interesting applications

67. Project Giant Reap
Image: CC BY-SA: http://free-photos.gatag.net/2013/12/05/110000.html

69. Space × Oriental Medicine

70. Giant Leap = 3D Printed Reflexology Sandals

71. 56,400 JPY = 14,000 TWD = 455 USD
[DMM2]
http://make.dmm.com/item/242572/

72. [Maker]
http://makezine.jp/event/mft2015/

73. "That's one small step for [a] man,
one giant leap for mankind.”
Neil Armstrong

74. References
[ISAC] https://2015.spaceappschallenge.org/
[PyData.Tokyo] http://pydatatokyo.connpass.com/
[PyData.Okinawa] http://pydataokinawa.connpass.com/
[PlaY data] http://www.meetup.com/playdata/
[PyCon JP] https://pycon.jp/2015/ja/talks/cfp/
[Face on Mars] http://en.wikipedia.org/wiki/Cydonia_%28region_of_Mars%29
[NASA/JPL] http://www.jpl.nasa.gov/spaceimages/
[Human on Mars] http://www.space.com/4876-female-figure-mars-rock.html
[Geoface Project] https://sites.google.com/site/geofaceproject/

75. References
[OpenCV] https://github.com/kylemcdonald/AppropriatingNewTechnologies/wiki/
Week-2
[LROC] http://target.lroc.asu.edu/q3/
[Wikipedia] http://en.wikipedia.org/wiki/Face_on_Moon_South_Pole
[DMM1] http://make.dmm.com/item/116196/
[Kaggle] https://www.kaggle.com/c/dogs-vs-cats
[Model Zoo] http://caffe.berkeleyvision.org/model_zoo.html
[SuperVision] A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification
with deep convolutional neural networks. NIPS, 2012.
[Notebook] http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/
filter_visualization.ipynb

76. References
[Cat & Dog] http://netgeek.biz/archives/25449
[Selective Search] Jasper R. R. Uijlings, Koen E. A. van de Sande, Theo Gevers,
Arnold W. M. Smeulders. Selective Search for Object Recognition. IJCV, 2013.
[R-CNN] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies
for accurate object detection and semantic segmentation. CVPR, 2014.
[DMM2] http://make.dmm.com/item/242572/
[Maker] http://makezine.jp/event/mft2015/