veggie vision by ibm

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Veggie Vision by IBM

Ideas about a practical system to make more efficient the selling and

inventory of produce in a grocery store.

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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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Up to 400 produce types

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Practical problems of application environment

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Engineering the solution

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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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Modifying the scale

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Need careful lighting engineering

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Need to segment product from background, even through plastic

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Previously published thresholding decision

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Quality segmented image obtained

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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 n  Sample product represented by concatenated

histograms: about 400 D n  350 produce items x 10 samples = 3500

feature vectors of 400D each n  Have about 2 seconds to compare an

unknown sample to 3500 stored samples (3500 dot products)

n  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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Shape: histogram of curvature of boundary of produce

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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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Learning and adaptation n  System “easy” to train: show it produce

samples and tell it the labels. n  During service: age out oldest sample;

replace last used sample with newly identified one.

n  When multiple labeled samples match the unknown, system asks cashier to select from the possible choices.

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Extension of Veggie Vision n  http://www.internetnews.com/xSP/article.php/

3642386 n  System uses almost all color features n  Installed in few places: many stores have self-

checkout, putting work on customer. n  IBM has a “shopping research” unit

http://www.usatoday.com/tech/news/techinnovations/2003-09-26-future-grocery-shop_x.htm

n  Customers will tolerate a higher human error than a machine error