1. Learn how to make your drawings come alive…  NEW COURSE: SKETCH RECOGNITION Analysis, implementation, and comparison of sketch recognition algorithms, including feature-based, vision-based, geometry-based, and timing-based recognition algorithms; examination of methods to combine results from various algorithms to improve recognition using AI techniques, such as graphical models.

2. Sutherland Electrical Engineering –Easier to draw than define –Too many lower level shapes –Dictionary (VK)

3. Rubine Method 1991 Foundation work in sketch recognition Used and cited widely Works well 15 samples adequate

4. Feature-based Gesture Recognition Statistical single stroke recognizer A lot of power from a single stroke Avoids segmentation problems User dependent

5. Rubine I gave you real data Tablets are faster now than they were Mouse was made to be slow For correct feel, pen needs to be fast

6. Rubine Implementation Issues? Duplicate location: f9, f10, f11 Duplicate time: f12 Divide by zero

7. Compare Answers

8. Mini Quiz G FED CAB H

9. Answers F1 D2 A3 C4 H5 B6 G7 E8

10. Rubine Discussion Non-intuitive data format User dependent – Have to learn a specific way of drawing or train data for each user Sketcher can’t draw naturally How you draw something is more important than what you draw Single stroke

11. Rubine Classification Evaluate each gesture 0 <= c <= C. V c = value = goodness of fit for that gesture c. Pick the largest V c, and return gesture c

12. Rubine Classification W c0 = initial weight of gesture W ci = weight for the I’th feature F i = i th feature value Sum the features together

13. Collect E examples of each gesture (e should be 15 according to paper) Calculate the feature vector for each example F cei = the feature value of the i th feature for the e th example of the c th gesture

14. Find average feature values for gesture For each gesture, compute the average feature value for each feature F ci is the average value for the i th feature for the c th gesture

15. Compute gesture covariance matrix How are the features of the shape related to each other? Look at one example - look at two features – how much does each feature differ from the mean – take the average for all examples – that is one spot in the matrix http://mathworld.wolfram.com/Covariance.html Is there a dependency (umbrellas/raining)

16. Normalize cov(X) or cov(X,Y) normalizes by N-1, if N>1, where N is the number of observations. This makes cov(X) the best unbiased estimate of the covariance matrix if the observations are from a normal distribution.For N=1, cov normalizes by N They don’t normalize for ease of next step (so just sum, not average)

17. Syllabus http://www.cs.tamu.edu/faculty/hammond/ courses/SR/2006http://www.cs.tamu.edu/faculty/hammond/ courses/SR/2006

18. Homework Make sure no old homework is missing Fix your feature data to match that of class Implement trainer –Data: 26 gestures; 15 examples –F cei = Compute 13 features for 26*15 examples –F ci = Compute average feature value for 13*26 features –E cij = Compute un-normalized covariance matrix (26*13*13) Turn in: Code and values for letter data