1 / 19

Advanced Sketch Recognition: Algorithms, Implementation, and AI Techniques

This course offers a comprehensive analysis and implementation of various sketch recognition algorithms, including feature-based, vision-based, geometry-based, and timing-based methods. Additionally, it covers techniques to combine results across algorithms to enhance recognition accuracy using AI techniques, such as graphical models. Participants will learn to make their drawings come alive through practical exercises involving Rubine classification, covariance matrix computation, and the normalization of gesture features. Hands-on assignments ensure a solid grasp of the material and encourage the exploration of innovative features for improved recognition.

didrika
Télécharger la présentation

Advanced Sketch Recognition: Algorithms, Implementation, and AI Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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. Learn how to make your drawings come alive…

  2. Rubine Features • Make sure your to convert to double before dividing in Java • Remove the second point not the first for duplicate points • Try to get your values as close to mine and move on.

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

  4. Rubine Classification • Wc0 = initial weight of gesture • Wci = weight for the I’th feature • Fi = ith feature value • Sum the features together

  5. Collect E examples of each gesture • (e should be 15 according to paper) • Calculate the feature vector for each example • Fcei = the feature value of the ith feature for the eth example of the cth gesture

  6. Find average feature values for gesture • For each gesture, compute the average feature value for each feature • Fci is the average value for the ith feature for the cth gesture

  7. 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)

  8. 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)

  9. Normalization • Taking the average • But… we want to find the true variance. • Note that our sample mean is not exactly the true mean. • By definition, our data is closer to the sample mean than the true mean • Thus the numerator is too small • So we reduce the denominator to compensate

  10. Common Covariance Matrix • How are the features related between all the examples? • Top = non normalize total covariance • Bottom = normalization factor = total number of examples – total number of shapes = 26*14

  11. Weights • Wcj = weight for the jth feature of the cth shape • Sum for each feature • Common Covariance Matrix inverted* ij • Average feature value for the ith feature for the cth gesture

  12. Initial Weight • Initial gesture weight = • Sum for each feature in class: • Feature weight * average feature value

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

  14. Rubine Classification • Wc0 = initial weight of gesture • Wci = weight for the I’th feature • Fi = ith feature value • Sum the features together

  15. Eliminate Jiggle • Any input point within 3 pixels of the previous point is discarded

  16. Rejection Technique 1 • If the top two gestures are near to each other, reject. • Vi > Vj for all j != i • Reject if less than .95

  17. Rejection Technique 2 • Mahalanobis distance • Number of standard deviations g is from the mean of its chosen class i.

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

  19. Homework • Fix covariance matrix • Implement trainer • Data: 26 gestures; 15 examples • Eij = Compute common covariance matrix (13*13) • Wci = Compute weights for each feature (26*13) • Wc0 = Compute initial weights for each class (26) • Build Classifier • Data: 2 gestures for each letter + 8 random = 60 examples • Vc = Compute value for each gesture • Classify with highest gesture number • Turn in: Code & classification and value for highest value • For Friday: Build option jitter reducer, rerun your data on the jitter reducer. Comment on any differences. • For Friday: Implement rejection • For Friday: Read Chris Long paper • For Monday: Come up with your own features. Compare your results.

More Related