a robust approach for local interest point detection in line-drawing images
DESCRIPTION
A Robust Approach for Local Interest Point Detection in Line-Drawing Images . The Anh Pham, Mathieu Delalandre , Sabine Barrat and Jean-Yves Ramel RFAI group- Polytech’Tour , France. CIL Talk Wednesday 7 th March 2012 Athens , Greece. Overview. Introduction - PowerPoint PPT PresentationTRANSCRIPT
A Robust Approach for Local Interest Point Detection in Line-
Drawing Images
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The Anh Pham, Mathieu Delalandre, Sabine Barrat and Jean-Yves RamelRFAI group- Polytech’Tour, France.
CIL TalkWednesday 7th March 2012
Athens, Greece
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Overview
Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works
Introduction (1)
This work is interested with graphic documents, especially the line drawings, some examples
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Introduction (2)
Interest points are a kind of local features (i.e. an image pattern which differs from its immediate neighborhood).
Popular interest points include edges, blobs, regions, salient points, etc. In graphics documents, interest points are end-points, corners and junctions:
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Local interest points
Approach Corner Junction RobustnessHigh curvature detection
[The-Chin89] ++++
Intensity-based methods [Harris89] ++
Model-based methods[Chul05] +++
Segmentation-based methods [Burge98] ++
Contour matching methods [Ramel00] ++
Tracking methods [Song02] ++
Comparison of the approaches for corner and junction detection
Introduction (3)5
Key idea of the work is to drive high curvature detection methods to achieve junction detection.Two problems:
(1) How to extract the curves(2) How to merge the multiple detections
High curvature detection is the task of segmenting a curve at distinguished points of high local curvature (e.g. corners, bends, joints).
High curvature detection methods often includes include polygonal and B-splines approximation, wavelet analysis, etc.
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Overview
Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works
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Junction detection in line-drawing images (1)
Flow-work of our approach
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Junction detection in line-drawing images (2)
Skeletonization, branch linking
High curvature detection
Path extraction
1D signals
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidates
(1) Skeletonization based on Di Baja (3,4)-chamfer distance [DiBaja94]
(2) Branch linking and Skeleton Connective Graph Construction (SCG) based on [Popel02]
Skeleton Connective Graph (SCG):
node: ended and crossing points
edge: skeleton branch
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Junction detection in line-drawing images (3)
Path definition: a sequence of edges of SCG that describes a complete stroke or a circuit.Three types of paths: Stroke path, Circuit path and Hybrid path.
Paths are extracted using anticlockwise direction between the nodes of graph SCG:
A skeleton graph
A stroke path A circuit path
Skeletonization, branch linking
High curvature detection
Path extraction
1D signals
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidatesd0
are branch pixelsare branch extremitiesis a crossing pixel
d0 is the extremity-crossing direction
Junction detection in line-drawing images (4)
Skeletonization, branch linking
High curvature detection
Path extraction
1D signals
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidates
A 2D path P consists in N points: (x1y1), (x2y2),…,(xNyN) To represent a 2D path in 1D signal, we selected the Rosenfeld-Johnston method:
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pi-q
pi pi+q
tqa
tqb
tqtq
tqtq
ba
baar
cos
,, qttqtttq yyxxa
,, qttqtttq yyxxb
tqtq
tqtq
ba
batf
)cos()(
pI-q pI pI+qtqa
tqb
f(t)straight-line -1high curvature
/2 0 pI-q pI
pI+q
tqa
tqb
Junction detection in line-drawing images (5)
Skeletonization, branch linking
High curvature detection
Path extraction
1D signals
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidates
Due to the q parameter, we must make the method shift invariant.To do so, we select starting point of lowest curvature i.e. f(t)-
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A good starting point here (shift-invariant).
Not goodstarting point.
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Skeletonization, branch linking
High curvature detection
Path extraction
1D signals
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidates
Using multi-resolution wavelet analysis because of its robustness and scale invariance (i.e. multi-resolution)[Gao06].
Junction detection in line-drawing images (6)
1D representationImage (I) 2D curcuit path
Multi-resolution wavelet analysis
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(1) Single path level: Remove the “unreliable” segments (i.e. length less than line thickness) and Connect the “reliable” segments togethers.
(2) Inter-path level (using voting scheme): merging close junctions together based on line thickness.
Junction detection in line-drawing images (7)
Skeletonization, branch linking
High curvature detection
Path extraction
Skeleton graph
Path representation
2D paths
Refining & Correcting
Candidates
1D signals
a path with high curvature points
a SCG with high curvature points
result after removing short segments
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Overview
Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works
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Experiments and Results (1)
Evaluation protocol:Evaluation Criteria is the repeatability score [Schmid00]
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2
p is a model pointq is a detected point
pq
Detection of p ispositive if d(p,q)<with d(p,q) the Euclidean distance
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Experiments and Results (2)
Datasets:
Logos-UMD ISRC2011Models 106 150Degradation
Rotation + Scaling + Kanungo noise
Test images 1272 3600
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Experiments and Results (3)
Some results
+ Liu99: “Identification of Fork point on the Skeletons of Handwritten Chinese Characters”, PAMI (1999).+ Haris detector: “A combined corner and edge detector”. Alvey Vision Conference, (1988).
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Experiments and Results (4)
Some visual results
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Overview
Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works
Conclusions and future works
Conclusions: A junction detector is proposed for line-drawing images The obtained results are rather promising
Future works The method is threshold dependent, we are looking for threshold adaptation
(e.g. region of support Improve the robustness of the merging step using topological analysis
(e.g. line bending energy minimization) More experiments with more interest points detector and datasets Applications of recognition of spotting (logos, symbols) and image indexing
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Thank you for your attention!