"Marsface Project: Detecting Pseudo-artificial Structures on Mars" at PyCon APAC 2015 https://tw.pycon.org/2015apac/en/program/71 Keywords Machine Learning, Object Recognition, Face Recognition, Artificial Intelligence (AI) ディープラーニング, 深層学習, 機械学習, 画像認識, 物体認識, 顔認識, 人工知能

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/