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Statistics and Shape Analysis - PowerPoint PPT Presentation

Statistics and Shape Analysis. By Marc Sobel. Shape similarity. Humans recognize shapes via both local and global features.

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Presentation Transcript

Statistics and Shape Analysis

By Marc Sobel

• Humans recognize shapes via both local and global features.

• (i) matching local features between shapes like curvature, distance to centroid can be statistically modeled via building statistics and parameters to reflect the matching.

• (ii) matching the relationship between global features of shapes (are they both apples or not?)

• How can we incorporate both local and global features in shape matching?

• An obvious paradigm is to model global features as governed by priors, and local features given global features as a likelihood.

Definitions and Notation matching

• Let u1,…,un be the vertices of one shape and v1,…,vm the vertices of another shape. We’d like to biuld correspondences between the vertices which properly reflect the relationship between the shapes. We use the notation (ui,vj) for a correspondence of this type. We use the terminology for a particle consisting of a set of such correspondences.

• Let Xi,l be the l’th local feature measure for vertex i of the first shape and Yj,l the l’th local feature measure for vertex j of the second shape. For now assume these feature measures are observed.

• We’d like to biuld a particle which reflects the local and global features of interest.

• If shapes result from one another via rotation and scaling then the order of shape 1 correspondence points should match the order of shape 2 correspondence points: i.e., if (i1,j1) is one correspondence and (i2,j2) is another, then either i1<i2 and j1<j2 or i1>i2 and j1>j2. We can incorporate this into a prior.

Notation: matching

• We have that:

Simple Likelihood matching

• Based on the observed features we form weight statistics:

• Let W denote the weight matrix associated with the features.

• Therefore given that a correspondence ‘C’ belongs in the ‘true’ set of correspondences, we write the simple likelihood in the form,

Complicated Likelihoods matching

• At stage t, putting ω as the parameter, we define the likelihood:

Simple and Complicated matchingPriors

• Model a prior for all sets of correspondences which are strongly contiguous:

• a) a simple prior (we use ω for the weight variable)

• b) I] a prior giving more weight to diagonals than other correspondences.

• II] we can define such a prior sequentially based on the fact that

Complicated Prior matching

• Put

• With ‘DIAG[i,j]’ referring to the positively oriented diagonal to which (i,j) belong.

• We would like to simulate the posterior distribution of contiguous correspondences. We do this by calculating the weights:

• Here we simulate:

A Simpler Model matching

• Define the posterior probabilities:

• For parameter λ, described below.

Weights for the simpler model matching

• The weights for the simpler model are particularly easy:

• Choosing λ tending to infinity properly, we get convergence to the MAP estimator of the simple particle filter.

Shape Similarity: A More complicated model employing curvature and distance parameters

• We have:

Simple Likelihood curvature and distance parameters

• Based on the observed features we form weight parameters:

• Let W denote the weight matrix associated with the features.

• Therefore given that a correspondence ‘C’ belongs in the ‘true’ set of correspondences, we write the likelihood in the form,

Particle Likelihood curvature and distance parameters

• We write the likelihood in the form:

Particle Prior curvature and distance parameters

• We assume standard priors for the mu’s and nu’s. We also assume a prior for the set of contiguous correspondences.

• The particle is updated as follows: define,

Particle Prior curvature and distance parameters

• At stage t we have particles,

• Their weights are given by: