1. CSE 803 Fall 2008 Stockman1 Veggie Vision by IBM Ideas about a practical system to make more efficient the selling and inventory of produce in a grocery store.

2. CSE 803 Fall 2008 Stockman2 Problem is recognizing produce

3. CSE 803 Fall 2008 Stockman3 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.

4. CSE 803 Fall 2008 Stockman4 Up to 400 produce types

5. CSE 803 Fall 2008 Stockman5

6. 6 Practical problems of application environment

7. CSE 803 Fall 2008 Stockman7 Engineering the solution

8. CSE 803 Fall 2008 Stockman8 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

9. CSE 803 Fall 2008 Stockman9 Modifying the scale

10. CSE 803 Fall 2008 Stockman10 Need careful lighting engineering

11. CSE 803 Fall 2008 Stockman11 Need to segment product from background, even through plastic

12. CSE 803 Fall 2008 Stockman12 Previously published thresholding decision

13. CSE 803 Fall 2008 Stockman13 Quality segmented image obtained

14. CSE 803 Fall 2008 Stockman14 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.

15. CSE 803 Fall 2008 Stockman15 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)

16. CSE 803 Fall 2008 Stockman16 HSI for pixel color: 6 bits for hue, 5 for saturation and intensity For each pixel quantify H HIST[H]++ same for S&I

17. CSE 803 Fall 2008 Stockman17 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)

18. CSE 803 Fall 2008 Stockman18 Texture: histogram results of LOG filter[s] on produce pixels Leafy produce A Leafy produce B

19. CSE 803 Fall 2008 Stockman19 Shape: histogram of curvature of boundary of produce

20. CSE 803 Fall 2008 Stockman20 Banana versus lemon or cucumber versus lime Large range of curvatures indicates complex object Small range of curvatures indicates roundish object

21. CSE 803 Fall 2008 Stockman21 Size is also represented by a histogram

22. CSE 803 Fall 2008 Stockman22 Each pixel gets a “size” as the minimum distance to boundary Purple grapes Chinese eggplants

23. CSE 803 Fall 2008 Stockman23 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.