2. Digital images are comprised of pixels
image tutorial
Bit is binary 1 or 0
Image is 10 pix across x 11 tall pix = 110 pixels
If it were 10” across x 11” tall then = 1 DPI
3. One byte in an 8-bit image has 8 slots or 28 = 256 combinations of 1/0
Bit #7 1/0
Bit #6 1/0
Bit #5 1/0
Bit #4 1/0
Bit #3 1/0
Bit #2 1/0
Bit #1 1/0
Bit #0 1/0
8-bit 16-bit 64-bit
These can be expressed as shades of gray or colors
One byte in a 32-bit image 232 = 4.29 x 109 combinations of 1/0
One byte in a 16-bit image has 216 = 65,536 combinations of 1/0
information content aka increasingly realistic representation of reality
8. Mapping genotype to phenotype:
Actually Phenotype space is enormous
=
Genotype
(all gene interactions)
9. Mapping genotype to phenotype:
Phenotype is at least Genotype x Environment
B73 maize grown in 3 environments
10. Mapping genotype to phenotype:
Phenotype space is enormous
=
Genotype
(all gene interactions)
x
Environment
11. Varieties of the phenotypic
experience: small scale
Atomic force
microscopy
High content cell screening
2D gel
electrophoresis
arstechnica.com
12. Varieties of the phenotypic
experience: Field Scale
Field phenotyping
with hyperspectral
imaging
13. Mapping genotype to phenotype:
Phenotype space is enormous
=
Genotype
(all gene interactions)
x
Environment
x
Scale
(from submolecular to ecological scales)
15. Mapping genotype to phenotype:
Phenotype space is enormous
=
Genotype (all gene interactions)
x
Environment
x
Scale (from submolecular to ecological scales and those
interactions)
x
Time (dynamics)
27. Mapping genotype to phenotype:
Phenotype space is enormous
=
Genotype (all gene interactions)
x
Environment
x
Scale (from submolecular to ecological scales and those
interactions)
x
Time (dynamics)
x
The way in which we study it (phenotyping approach)
28. Video informatics uses imaging and computational
methods to quantify complex and dynamic phenotypes
Sportsvision
29. Video informatics uses imaging and computational
methods to quantify complex and dynamic phenotypes
39. RAW DATA ACQUISITION – computer cannot distinguish object of
interest from background
40. IMAGE PROCESSING – computer can now distinguish object of interest
from background
41. IMAGE ANALYSIS – Feature Extraction: object of interest measured in
different ways
42. Plant shape metrics
Pseudocolored by NIR signal
Image background
Original image
After thresholding and erosion steps
After binary thresholding
Sobel filtered (x-axis)
Sobel filtered (y-axis)
Sobel filtered (x and y-axis)
Laplacian filtered
Laplacian sharpened
Sobel filtered inverted
After background subtraction
Laplacian sharpened + Sobel filtered
Final plant isolated
Object identification
Image maskBitwise OR join of both methods Original image
Supplemental Figure S11. A visual illustration of the pipeline used to threshold plant tissue from background within grayscale NIRFahlgren, Feldman, Gehan et al. Plant Phys. In press
Image analysis pipeline