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Today: Stochastic Processes
Two Topics
• CONDENSATION
• Modeling texture
Monday (3/5)
• Reflectance
• Environment Matting
Final Project Presentations
• Tentatively: Monday March 12, 2:30-4:30
Tracking
We’ve seen what tracking can do
Where does it fail?
• occlusions
• fast motions
• ambiguity
Need to be able to recover from failures
• multiple hypothesis tracking
Modeling Texture
What is texture?
• An image obeying some statistical properties
• Similar structures repeated over and over again
• Often has some degree of randomness
What Do These Have in Common
Tracking and texture can both be modeled
by stochastic processes
Stochastic Process Terminology
Random variable
•
•
•
•
nondeterministic value with a given probability distribution
e.g. result of roll of dice
discrete: can take on a finite number of possible values
continuous: not discrete
Probability density function (PDF)
• non-negative real valued function p(x) such that
• called probability distribution when x is discrete
• sometimes probability distribution is used to refer to densities
Discrete stochastic process
• sequence or array of random variables, statistically interrelated
• e.g. states of atoms in a crystal lattice
Conditional probability
• P[A|B,C] means probability of A given B and C
• e.g. probability of snow today given snow yesterday and the day
before
Statistical Inference
Estimation
• Given measurements z = (z1, z2, ..., zn)
• Compute model parameters x = (x1, x2, ..., xm)
Statistical estimation
• Given measurements z and uncertainty information
• Compute p(x | z) – probability of every possible model
Key Tool: Bayes Law
likelihood
(what measurement would we expect
to see if we knew the model?)
prior
posterior
(our knowledge
about these
parameters)
(what’s the
model?)
normalization
MAP Estimation
Often we just want to maximize p(x|z)
• Can ignore p(z), since it’s a constant (doesn’t depend on x)
• Maximum a posteriori (MAP) estimate of x is
What if no prior information?
• p(x) is constant for all x (a uniform distribution)
• Posterior  likelihood term
• Maximum likelihood estimate of x is
Markov Process
1D stochastic process
• a sequence of interdependent random variables xt, xt-1, xt-2, ..., x1
• xt is the state of the model at time t
Markov assumption
• model state depends depends only on a fixed (finite) number
of previous states
• First order Markov Chain
• N th order Markov Chain
Putting It To Work
Text Synthesis
• Create plausible looking poetry, love letters, term papers,
congressional reports, etc.
• Learn
– find all blocks of N consecutive words/letters in training
documents
– build probabilities of these N-tuples from histogram
» probability of N-tuple X = (# of occurances of X / total # N-tuples)
– Create K th word by taking a random sample from
» possible methods
»
»
»
»
invert cumulative probability distribution (must be invertable to work)
rejection sampling
use importance sampling (e.g., Metropolis Algorithm)
simpler: just remember the most likely N tuples beginning with
previous N-1 words
[Scientific American, June 1989, Dewdney]
“I Spent an Interesting Evening Recently with a Grain of Salt”
- Mark V. Shaney
(computer-generated contributor to UseNet News group called net.singles)
Output of 2nd order word-level Markov Chain after training on 90,000
word philosophical essay:
“If we were to revive the fable is useless. Perhaps only the allegory of
simulation is unendurable--more cruel than Artaud's Theatre of Cruelty,
which was the first to practice deterrence, abstraction, disconnection,
deterritorialisation, etc.; and if it were our own past. We are witnessing
the end of the negative form. But nothing separates one pole from the
very swing of voting ''rights'' to electoral...”
Probabilistic Tracking
Treat tracking problem as a Markov process
• Estimate p(xt | zt, xt-1)
• Combine Markov assumption with Bayes Rule
measurement likelihood
prediction
(likelihood of seeing
this measurement)
(based on previous
frame and motion model)
Approach
• Predict position at time t:
• Measure (perform correlation search or Lukas-Kanade) and
compute likelihood
• Combine to obtain (unnormalized) state probability
Kalman Filtering:
assume p(x) is a Gaussian
initial state
prediction
measurement
posterior
prediction
Key
• s = x (position)
• o = z (sensor)
[Schiele et al. 94], [Weiß et al. 94], [Borenstein
96], [Gutmann et al. 96, 98], [Arras 98]
Robot figures courtesy of Dieter Fox
Modeling Probabilities with Samples
Allocate samples according to probability
• Higher probability—more samples
CONDENSATION [Isard & Blake]
Initialization: unknown position (uniform)
Measurement
posterior
CONDENSATION [Isard & Blake]
Prediction:
• draw new samples from the PDF
• use the motion model to move the samples
CONDENSATION [Isard & Blake]
Measurement
posterior
Monte Carlo Robot Localization
Particle Filters [Fox, Dellaert, Thrun and collaborators]
CONDENSATION Contour Tracking
Training a tracker
CONDENSATION Contour Tracking
Red: smooth drawing
Green: scribble
Blue: pause
Modeling Texture
What is texture?
• An image obeying some statistical properties
• Similar structures repeated over and over again
• Often has some degree of randomness
2D Stochastic Process Terminology
Random field
• multi-dimensional stochastic process
– e.g., each pixel a random variable.
• a random field is stationary if statistical relationships are
space-invariant (translate across the image)
Markov random field
• random field where each variable is conditioned on a finite
neighborhood
MRF’s and Images
A Markov random field (MRF)
• generalization of Markov chains to two dimensions.
Homogeneous (stationary) first-order MRF:
• probability that pixel X takes a certain value given the values
of neighbors A, B, C, and D:
A
• P[X|A,B,C,D]
D
X
B
C
• Higher order MRF’s have larger neighborhoods
*
*
*
*
*
X
*
*
*
*
*
*
*
*
*
X
*
*
*
*
*
*
Modeling MRF’s
Given training image(s)
• compute p(X), p(X | A, B, C, D) based on training set
– by creating histograms of pixels, neighborhoods
Not quite what we want
• We’d like a joint probability function over the entire image:
– p(X11, X12, X13, ...)
– If we had this, we could just sample random images from it
• Turns out this function is a Gibbs distribution
– [Geman & Geman, 1984]
• However, still hard to sample from
– poor convergence
– can be very slow—hours, days...
Texture Synthesis [Efros & Leung, ICCV 99]
Simpler algorithm, avoids Gibbs sampling
Synthesizing One Pixel
SAMPLE
p
Infinite sample
image
Generated image
• Assuming Markov property, what is conditional probability distribution of p,
given the neighbourhood window?
• Instead of constructing a model, let’s directly search the input image for all
such neighbourhoods to produce a histogram for p
• To synthesize p, just pick one match at random
Slides courtesy of Alyosha Efros
Really Synthesizing One Pixel
SAMPLE
finite sample
image
p
Generated image
• However, since our sample image is finite, an exact neighbourhood match
might not be present
• So we find the best match using SSD error (weighted by a Gaussian to
emphasize local structure), and take all samples within some distance from
that match
Growing Texture
• Starting from the initial image, “grow” the texture one pixel at a time
• The size of the neighborhood window is a parameter that specifies how
stochastic the user believes this texture to be
Some Details
Growing is in “onion skin” order
• Within each “layer”, pixels with most neighbors are synthesized first
• If no close match can be found, the pixel is not synthesized until the
end
Using Gaussian-weighted SSD is very important
• to make sure the new pixel agrees with its closest neighbors
• Approximates reduction to a smaller neighborhood window if data is
too sparse
Window Size Controls Randomness
More Synthesis Results
Increasing window size
Brodatz Results
reptile skin
aluminum wire
Failure Cases
Growing garbage
Verbatim copying
Image-Based Text Synthesis
Other Texture Synthesis Methods
Heeger & Bergen, SIGGRAPH 95
• Early work, got people interested in problem
DeBonet, SIGGRAPH 97
• Breakthrough method, surprisingly good results
• More complex approach, more parameters (filter banks)
Wei & Levoy, SIGGRAPH 99
• Similar to Efros and Leung, but faster
• Uses coarse-to-fine, vector quantization
Zhu et al., IJCV 98 (and other papers)
• Rigorous MRF algorithms, Gibbs sampling
• Well-founded statistically, but slower
Others...
Applications of Texture Modeling
Super-resolution
• Freeman & Pasztor, 1999
• Baker & Kanade, 2000
Image/video compression
Video Textures
• Wei & Levoy, 2000
• Schodl et al., 2000
Texture recognition, segmentation
• DeBonet
Restoration
• removing scratches, holes, filtering
• Zhu et al.
Art/entertainment