Learn how to make your drawings come alive…

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# Learn how to make your drawings come alive… - PowerPoint PPT Presentation

. NEW COURSE: SKETCH RECOGNITION

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Presentation Transcript

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…

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.
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
Rubine Classification
• Wc0 = initial weight of gesture
• Wci = weight for the I’th feature
• Fi = ith feature value
• Sum the features together
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
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
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)
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)
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
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
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
Initial Weight
• Initial gesture weight =
• Sum for each feature in class:
• Feature weight * average feature value
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
Rubine Classification
• Wc0 = initial weight of gesture
• Wci = weight for the I’th feature
• Fi = ith feature value
• Sum the features together
Eliminate Jiggle
• Any input point within 3 pixels of the previous point is discarded
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
Rejection Technique 2
• Mahalanobis distance
• Number of standard deviations g is from the mean of its chosen class i.
Syllabus
• http://www.cs.tamu.edu/faculty/hammond/courses/SR/2006
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.