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© Leo Burnett. 2 “Moe”: April 23 “Moe” : June 6 3  Population size estimation  Behavioral studies  Phenotype analysis.

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Presentation on theme: "© Leo Burnett. 2 “Moe”: April 23 “Moe” : June 6 3  Population size estimation  Behavioral studies  Phenotype analysis."— Presentation transcript:

1 © Leo Burnett

2 2 “Moe”: April 23 “Moe” : June 6

3 3  Population size estimation  Behavioral studies  Phenotype analysis

4 4 Tracking devices:  Require anesthetization  Expensive  Unreliable  Broad coverage all but impossible

5 5 1.Moe93% 2.Alice5% 3.Bob2% The “ALGORITHM” Database of zebra pictures Input – “the query” Output – “zebra ranking”

6 6  Face recognition algorithms ▪ Early algorithms too rigid ▪ Modern algorithm could work, but are black boxes  Fingerprint recognition ▪ Generally look for well-defined features ▪ Rarely deal with occlusion, perspective skew, varying distance to camera

7 7 © Colchester Zoo y x 1.Shape contour tracing 2.Spline function fitting 3.Query and retrieve splines

8 8

9 9  How much information is there in the data?

10 10  How much information is there in the data?

11 11  Hypothesis: width and spacing of stripes are distinctive when measured finely

12 12 Zebras are/have:  seldom two-dimensional  frequently obscured  non uniform stripes  high tendency towards pregnancy and violence  afraid of barcode scanners © Barcodeman

13 13 Build a solution by eliminating the problems!

14 14 Closer to camera, more pixels for the bodyFurther away, fewer pixels for the body 10 Megapixel camera = 3648 pixels across, 2736 pixels down 1080p HD TV = 1920 pixels across, 1080 pixels down 15” MacBook Pro screen= 1440 pixels across, 900 pixels down

15 15  Solution: measure widths relative to the previous stripe

16 16  Solution: measure widths relative to the previous stripe

17 17  Solution: measure widths relative to the previous stripe

18 18  Solution: measure widths relative to the previous stripe

19 19

20 20  Shear transformation flattens small amount of perspective skew [1]. Original imageWith shear transformation

21 21  Shear transformation flattens small amount of perspective skew [1].  Shear transformation is a special case of affine transformation.  Affine transformation:  Ratios of distances along a line are preserved

22 22  Solution: measure widths relative to the previous stripe

23 23  Solution: measure widths relative to the previous stripe A “strip” of stripes

24 From original photograph in database: Zebra occluded from the left side: Missed the rightmost black stripe: Extremely oblique viewing angle:

25 25  Solution: Dynamic programming  Align two strips to minimize “errors”  a.k.a. Spell-check, DNA sequence alignment, Needleman-Wunsch algorithm, Smith-Waterman algorithm, edit distance, dynamic time warping, etc.  Stripe-alignment!  occlusion = indel cost  image processing errors = indel + matching cost  stripe distortion = matching cost  strong perspective skew= matching cost  Low alignment “cost” = fewer differences in strips = zebras are very similar

26 COST = COST = ( ) + ( ) = 0.08

27 27 For a new picture (the “query”):  Read a strip off the body at a known location  Align against all the zebra strips in the database, also from the same location  Rank zebras in the database by the alignment cost of their strips

28 28  One click per zebra  Analogous to a barcode scanner  Handles occlusion, minor perspective skew  Can be applied to any part of the body  Computationally efficient

29 29  20 zebras, ~6 photos per zebra = 109 pictures. “Transcription” errors

30 30  Photos were manually identified by Rosemary at Ol’Pejeta Conservancy.  Manually coded stripes along the shoulder  For each photo, rank the closest matches using dynamic programming.  Metric: rank of the correct zebra in the list of closest matches.

31 31 Proportion of queries at or below rank Average rank = 1.5

32 32  No love from Zebra #3 – “01_700”  Photo 87 Zebra 3 Flank R PicID 8159 CORRECT_RANK 4  Photo 65 Zebra 3 Flank L PicID 8142 CORRECT_RANK 9  Photo 63 Zebra 3 Flank R PicID 8170 CORRECT_RANK 5  Photo 52 Zebra 3 Flank R PicID 8166 CORRECT_RANK

33 33  Worst performance on this picture:

34 34  Time to search database of 108 pictures:  seconds-- my ageing 2006 laptop  If the number of stripes on a zebra is O(1), then the time complexity of a single search is linear in the number of photographs.  1,000 pictures~ 0.2 seconds  10,000 pictures~ 3 seconds

35 35

36 36 Future work: 1. Build an effective user interface 2. Get field biologists to discover better ways to use it! 3. Run tests on 3,000+ pictures from January Kenya trip 4. Tweak image processing algorithms “So you see! There’s no end to the thing you might know, depending how far beyond Zebra you go.” - Dr. Seuss, Beyond Zebra References: [1] Hutchison and Barrett. Fourier-Mellin registration of line-detained tabular document images. Intl. J. Doc. Analysis 8(2):87-110,


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