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Ideas about a practical system to make more efficient the selling and inventory of produce in a grocery store. Veggie Vision by IBM. Problem is recognizing produce. properly charge customer do inventory save customer and checker time. 15+ years of R&D now.

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slide1
Ideas about a practical system

to make more efficient the selling and inventory of produce in a grocery store.

Veggie Vision by IBM

CSE 803 Fall 2013

problem is recognizing produce
Problem is recognizing produce
  • properly charge customer
  • do inventory
  • save customer and checker time

CSE 803 Fall 2013

15 years of r d now
CSE 803 Fall 201315+ 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.

engineering the solution
Engineering the solution

CSE 803 Fall 2013

system to operate inside the usual checkout station
CSE 803 Fall 2013System 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

design of pattern recognition paradigm from 1997
CSE 803 Fall 2013Design 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.

matching procedure
CSE 803 Fall 2013Matching 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)

hsi for pixel color 6 bits for hue 5 for saturation and intensity
CSE 803 Fall 2013HSI for pixel color: 6 bits for hue, 5 for saturation and intensity

For each pixel

quantify H

HIST[H]++

same for S&I

histograms of 2 limes versus 3 lemons
CSE 803 Fall 2013Histograms 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)
banana versus lemon or cucumber versus lime
CSE 803 Fall 2013Banana versus lemon or cucumber versus lime

Small range of curvatures indicates roundish object

Large range of curvatures indicates complex object

learning and adaptation
CSE 803 Fall 2013Learning 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.

where is veggie vision today
CSE 803 Fall 2013Where is Veggie Vision today?

http://www.internetnews.com/xSP/article.php/3642386

System uses almost all color features

Installed in few places: many stores have self-checkout, putting work on customer.

IBM has a “shopping research” unit http://www.usatoday.com/tech/news/techinnovations/2003-09-26-future-grocery-shop_x.htm

Customers will tolerate a higher human error than a machine error