CS 332 Visual Processing in Computer and Biological Vision Systems Hodgepodge

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)

Rowley, Baluja & Kanade: face detection with neural nets

Edwin Land’s color mondrian experiments Analysis of color Edwin Land’s color mondrian experiments

Land’s 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)

Measuring color by retinal cones

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 data’s variance Mathematically, the principle components are the eigenvectors of the covariance matrix of the original data set

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

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 + ΣciPi 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