Veggie Vision by IBM
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Transcript of Veggie Vision by IBM
CSE 803 Fall 2008 Stockman 1
Veggie Vision by IBM
Ideas about a practical systemto make more efficient the selling
and inventory of produce in a grocery store.
CSE 803 Fall 2008 Stockman 2
Problem is recognizing produce
• properly charge customer
• do inventory
• save customer and checker time
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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.
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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
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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.
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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)
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HSI for pixel color: 6 bits for hue, 5 for saturation and intensity
For each pixel
quantify H
HIST[H]++
same for S&I
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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)
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Texture: histogram results of LOG filter[s] on produce pixels
Leafy produce A
Leafy produce B
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Banana versus lemon or cucumber versus lime
Large range of curvatures indicates complex object
Small range of curvatures indicates roundish object
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Each pixel gets a “size” as the minimum distance to boundary
Purple grapes Chinese eggplants
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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.
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Where 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