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Learning Decompositional Shape Models from Examples. Alex Levinshtein Cristian Sminchisescu Sven Dickinson University of Toronto. Hierarchical Models. Manually built hierarchical model proposed by Marr And Nishihara - PowerPoint PPT Presentation
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Learning Decompositional Learning Decompositional Shape Models from Shape Models from
ExamplesExamples
Alex LevinshteinAlex LevinshteinCristian SminchisescuCristian Sminchisescu
Sven DickinsonSven Dickinson
University of TorontoUniversity of Toronto
Hierarchical ModelsHierarchical ModelsManually built hierarchical model proposed by Marr And Nishihara(“Representation and recognition of the spatial organization of three dimensional shapes”, Proc. of Royal Soc. of London, 1978)
Our goalOur goal
Automatically construct a generic hierarchical shape model from exemplars
Challenges:
Cannot assume similar appearance among different exemplars
Generic features are highly ambiguous
Generic features may not be in one-to-one correspondence
Automatically constructed Automatically constructed Hierarchical ModelsHierarchical Models
Input:
Question: What is it?
Output:
Stages of the systemStages of the systemExemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Blob Graph ConstructionBlob Graph ConstructionExemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Blob Graph ConstructionBlob Graph Construction
Edges are invariant to articulation
Choose the largest connected component.
On the Representation and Matching of Qualitative Shape at Multiple Scales
A. Shokoufandeh, S. Dickinson, C. Jonsson, L. Bretzner, and T. Lindeberg,ECCV 2002
Feature matchingFeature matchingExemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Feature matchingFeature matchingOne-to-one matching. Rely on shape and context, not appearance!
?
Many-to-many matching
Feature embedding and Feature embedding and EMDEMD
Spectral embedding
Returning to our set of Returning to our set of inputsinputs
Many-to-many matching of every pair of exemplars.
Part ExtractionPart ExtractionExemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Results of the part extraction Results of the part extraction stagestage
What is next?What is next?
Extracting attachment Extracting attachment relationsrelations
Exemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Extracting attachment Extracting attachment relationsrelations
Right arm is typically connected to torso in exemplar images !
Extracting decomposition Extracting decomposition relationsrelations
Exemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
Extracting decomposition Extracting decomposition relationsrelations
Model construction stage Model construction stage summarysummary
Model Construction:
Clustering blobs into parts based on one-to-one matching results.
Recovering relations between parts based on individual matching and attachment results.
Assemble Final ModelAssemble Final ModelExemplar imagesExtract Blob
GraphsMatch Blob
Graphs (many-to-many)
Extract PartsExtract
Decomposition Relations
Extract Attachment Relations
Assemble Final Model
Blob graphs
Many-to-many correspondences
Model parts
Model decomposition relations
Model attachment relations
ConclusionsConclusions General framework for constructing a General framework for constructing a
generic decompositional model from generic decompositional model from different exemplars with dissimilar different exemplars with dissimilar appearance.appearance.
Recovering decompositional relations Recovering decompositional relations requires solving the difficult many-to-many requires solving the difficult many-to-many graph matching problem.graph matching problem.
Preliminary results indicate good model Preliminary results indicate good model recovery from noisy features.recovery from noisy features.
Future workFuture work Construct models for objects other than Construct models for objects other than
humans.humans.
Provide scale invariance during matching.Provide scale invariance during matching.
Automatically learn perceptual grouping Automatically learn perceptual grouping relations from labeled examples.relations from labeled examples.
Develop indexing and matching framework Develop indexing and matching framework for decompositional models.for decompositional models.