CS 332 Visual Processing in Computer and Biological Vision Systems

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CS 332 Visual Processing in Computer and
Biological Vision Systems

• Hodgepodge

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Artificial Neural Nets
Use feedforward network to compute output from
inputs
Use back-propagation algorithm to learn weights
from training data (correct input/output pairs)
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Rowley, Baluja Kanade face detection with
neural nets
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Analysis of color
Edwin Lands color mondrian experiments
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Lands Retinex Theory of Color
L(x,y,?) I(x,y,?) R(x,y,?)
L(x,y,?) luminance I(x,y,?) illuminant R(x,y,?)
surface reflectance
Goal recover surface reflectance (color)
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Measuring color by retinal cones
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Principal components analysis

• ? Method for reducing the dimensionality of a
  high-dimensional data set, allowing a more
  compact representation of each element of the set
• ? Takes advantage of redundancy within a data set
• Expresses original data samples as a linear
  combination of a set of components that capture
  as much as possible of the datas variance
• Mathematically, the principle components are the
  eigenvectors of the covariance matrix of the
  original data set

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Troje Using PCA to represent human gait

• Obtain motion capture data from many human
  walkers
• ? Use PCA to construct a small number of
  eigenpostures
• ? Express each posture in the original motion
  sequence as a weighted sum of eigenpostures
• Use pattern of changing coefficients over time
  to recognize movements, e.g. classify gender

Troje walker demo
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Using eigenpostures to represent gaits
Each posture consists of (x,y,z) coordinates of
15 locations Each sequence consists of about 1400
postures (12 secs, 20 steps) PCA analysis first
component captures 84 of variance in postures,
first four components capture 98 of variance P
P0 SciPi P0 average posture Pi i-th
principal component, i 1..4 (four
eigenpostures) Ci coefficient of i-th
eigenposture Pattern of coefficients over time is
approximately sinusoidal demo varies properties
of sinusoids