Transcript week03-VeggieVisionIBM.ppt

Veggie Vision by IBM
Ideas about a practical system
to make more efficient the selling and
inventory of produce in a grocery store.
CSE 803 Fall 2008 Stockman
1
Problem is recognizing produce
CSE 803 Fall 2008 Stockman
2
15+ years of R&D now
This information was shared
by IBM researchers. Since
that time, the system has
been tested in small
markets and has been
modified according to that
experience.
CSE 803 Fall 2008 Stockman
3
Up to 400 produce types
CSE 803 Fall 2008 Stockman
4
CSE 803 Fall 2008 Stockman
5
Practical problems of
application environment
CSE 803 Fall 2008 Stockman
6
Engineering the solution
CSE 803 Fall 2008 Stockman
7
System to operate inside the
usual checkout station
• together with bar code scanner
• together with scale
• together with accounting
• together with inventory
• together with employee
• within typical store environment
* figure shows system asking for
help from the cashier in making
final decision on touch screen
CSE 803 Fall 2008 Stockman
8
Modifying the scale
CSE 803 Fall 2008 Stockman
9
Need careful lighting engineering
CSE 803 Fall 2008 Stockman
10
Need to segment product from
background, even through plastic
CSE 803 Fall 2008 Stockman
11
Previously published
thresholding decision
CSE 803 Fall 2008 Stockman
12
Quality segmented image obtained
CSE 803 Fall 2008 Stockman
13
Design of pattern recognition
paradigm (from 1997)
FEATURES are: color,
texture, shape, and size
all represented
uniformly by
HISTOGRAMS
Histograms capture
statistical properties of
regions – any number of
regions.
CSE 803 Fall 2008 Stockman
14
Matching procedure




Sample product represented by concatenated
histograms: about 400 D
350 produce items x 10 samples = 3500
feature vectors of 400D each
Have about 2 seconds to compare an
unknown sample to 3500 stored samples
(3500 dot products)
Analyze the k nearest: if closest 2 are from
one class, recognize that class (sure)
CSE 803 Fall 2008 Stockman
15
HSI for pixel color: 6 bits for hue,
5 for saturation and intensity
For each pixel
quantify H
HIST[H]++
same for S&I
CSE 803 Fall 2008 Stockman
16
Histograms of 2 limes
versus 3 lemons
Distribution or
population concept
adds robustness:
• to size of objects
• to number of
objects
• to small
variations of color
(texture, shape,
size)
CSE 803 Fall 2008 Stockman
17
Texture: histogram results of
LOG filter[s] on produce pixels
Leafy
produce B
Leafy
produce A
CSE 803 Fall 2008 Stockman
18
Shape: histogram of curvature
of boundary of produce
CSE 803 Fall 2008 Stockman
19
Banana versus lemon or
cucumber versus lime
Large range of curvatures
indicates complex object
Small range of curvatures
indicates roundish object
CSE 803 Fall 2008 Stockman
20
Size is also represented by a
histogram
CSE 803 Fall 2008 Stockman
21
Each pixel gets a “size” as the
minimum distance to boundary
Purple grapes
Chinese eggplants
CSE 803 Fall 2008 Stockman
22
Learning and adaptation



System “easy” to train: show it produce
samples and tell it the labels.
During service: age out oldest sample;
replace last used sample with newly
identified one.
When multiple labeled samples match
the unknown, system asks cashier to
select from the possible choices.
CSE 803 Fall 2008 Stockman
23