Exploiting T-Junctions For
Depth Segregation In Single
Images
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MARIELLA DIMICCOLI, PHILIPPE
SALEMBIER
PROCEEDINGS OF THE 2009 IEEE ICASSP
ISBN:978-1-4244-2353-8 (1229-1232)
Outline
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 Introduction
 Related Work
 Proposed Approach
 T-junction detection
 Segmentation
 Depth Ordering
 Experimental Results
 Conclusions and Future Work
Introduction
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 Developed a new algorithm for robust T-junction
detection in single images
 Developed a new segmentation strategy based on
region merging
Related Work
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 Markov Random Field (MRF) models
 Stella et al.
 Developed an ordered partitioning method in spectral graph
theory
 Gao et al.
 Proposed a Bayesian inference framework
 Learning-based approaches
 Rely on strong assumptions on the image structure
 On the use of human segmentation
Proposed Approach - T-junction detection
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 Candidate point selection
 Performed on the structural part of the image
( cartoon component )


Localized by the local filter SUSAN
Apply a dilation on the mask of candidate points obtained
by applying SUSAN
 Branch extraction and validation
 Performed by a region merging algorithm
 Three different criteria to validate the candidate point
Geometrical
 Branches orientation
 Assumption that object contours


k = wc
Proposed Approach - T-junction detection
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 Cluster reduction
 Varies smoothly
 Its neighbors have a high probability of being validated too
Proposed Approach - Segmentation
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 Computing a partition of the image through region
merging but preserving T-junctions
 Mainly defined by a statistical measure on the
regions
 Each region modeled by its color histogram
 Incompatibility


If two regions are wedges of a T-junction
Cannot be merged
Proposed Approach - Depth Ordering
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 To construct a global and consistent interpretation
from the local depth assessment previously obtained
 DG = (V, EA, A)
Experimental Results
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 Tested our algorithm on a set of real images
 Show four images
 The original image
 A gray level version of the original
 The segmented image
 The depthmap
Experimental Results
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Conclusions and Future Work
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 Conclusions
 Proposed a new approach for depth segregation in single
images
 Fully automatic and does not make any assumption on the
image structure
 Future work
 Improve the robustness of the proposed method and to obtain
a more detailed depthmap
Thanks For Your Listening !
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