Robust spectral 3D-bodypart segmentation along time Fabio Cuzzolin, Diana Mateus, Edmond Boyer, Radu...
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Robust spectral 3D- bodypart segmentation along time Fabio Cuzzolin, Diana Mateus, Edmond Boyer, Radu Horaud ICCV07 – HumanMotion Workshop 20/10/2007
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Transcript of Robust spectral 3D-bodypart segmentation along time Fabio Cuzzolin, Diana Mateus, Edmond Boyer, Radu...
Robust spectral 3D-bodypart segmentation
along time
Fabio Cuzzolin, Diana Mateus, Edmond Boyer, Radu Horaud
ICCV07 – HumanMotion Workshop20/10/2007
Robust spectral segmentation
Consistent bodypart segmentation in sequences Why clustering in the embedding space K-wise clustering Branch detection Seed propagation Merging-splitting clusters Algorithm Results Influence of d and K Topology changes Comparison with EM clustering Comparison with ISOMAP clustering
Problem
Segmenting bodyparts of moving articulated bodies along sequences, in a consistent way in an unsupervised fashion robustly, with respect to changes of
the topology of the moving body as a bulding block of a wider motion
analysis and capture framework
Clustering in the embedding space
Locally Linear Embedding: preserves the local Locally Linear Embedding: preserves the local structure of the datasetstructure of the dataset generates a lower-dim embedded cloud less sensitive to topology changes than other methods shape of the embedded cloud fairly stable under AND less computationally expensive then ISOMAP
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3D shape
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LLE space
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ISOMAP space
Pose invariance with LLE To ensure consistent To ensure consistent
segmentation the stability of the segmentation the stability of the embedded cloud is necessaryembedded cloud is necessary
rigid part
rigid part
moving joint area
unaffected neighborhoods
unaffected neighborhoods
affected neighborhoods
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0.15 LLE works with local LLE works with local neighborhoods -> neighborhoods -> stable under stable under articulated motionarticulated motion
Algorithm
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Original dataset
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Clustering in original spaceBranch terminations
Segmentation in the embedding space
a) b) c)
Pictorial illustration of the overall algorithm
K-wise clustering LLE maps the 3D shape to a lower-dimensional LLE maps the 3D shape to a lower-dimensional
shapeshape Idea: clustering collinear points togetherIdea: clustering collinear points together
K-wise clustering:K-wise clustering:
a hypergraph H is built by measuring the affinity of all triads a weighted graph G which approximates H is constructed by constrained
linear least square optimization the approximating graph is partitioned by spectral clustering (n-cut)
Branch detection and number of clusters
Branches can be detected easilyBranches can be detected easily
Branch termination not detected Branch termination detected
An embedded point is a termination if its projection on the line interpolating its An embedded point is a termination if its projection on the line interpolating its neighborhood is an extremumneighborhood is an extremum
Seed propagation along time
To ensure time To ensure time consistency clusters’ consistency clusters’ seeds have to be seeds have to be propagated along propagated along timetime
Old positions of Old positions of clusters in 3D are clusters in 3D are added to new cloud added to new cloud and embeddedand embedded
Result: new seedsResult: new seeds
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90X(t)
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90X(t+1)
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LLE(t) LLE(t+1)
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Merging/splitting clusters
1. At each t all branch terminations of Y(t) are detected;
2. if t=0 they are used as seeds for k-wise clustering;
3. otherwise (t>0) standard k-means is performed on Y(t) using branch terminations as seeds, yielding a rough partition of the embedded cloud into distinct branches;
4. propagated seeds in the same partition are merged;
5. for each partition of Y(t) not containing any old seed a new seed is defined as the related branch termination.
Results - 1
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Clustering in original space
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Cluster centroids trajectories
Results - 2
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Clustering in original space
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Cluster centroids trajectories
Estimating k
The number of neighbors can be estimated from the data sequenceThe number of neighbors can be estimated from the data sequence Admissible k: yields neighborhoods which do not span different
bodyparts
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Influence of dimension
Choosing a larger dimension for the embedding space Choosing a larger dimension for the embedding space improves the resolution of the segmentationimproves the resolution of the segmentation
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3D Segmentation
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Clustering in embedding space
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Change of topology When topology changes, clusters merge or When topology changes, clusters merge or
split to adaptsplit to adapt
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danceuse, t = 16
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Performance of EM clustering EM clustering fits a multi-Gaussian distribution EM clustering fits a multi-Gaussian distribution
to the data through the EM algorithmto the data through the EM algorithm
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Cluster centroids trajectoriest = 40 t = 55
Performance of ISOMAP
The same The same propagation propagation scheme can scheme can be applied in be applied in the ISOMAP the ISOMAP spacespace
extremely extremely sensitivesensitive to to topology topology changeschanges
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Segmentation in original space
Clustering in ISOMAP space
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Conclusions Unsupervised bodypart segmentation algorithm Unsupervised bodypart segmentation algorithm
which ensure consistency along timewhich ensure consistency along time Spectral method: clustering is performed in the
embedding space (in particular after LLE) as shape becomes lower-dim and different bodyparts are widely separated
Seeds are propagated along time and merged/splitted according to topology variations
Compares favorably with other techniques First step of motion analysis (matching, action
recognition, etc.)
