A Robust Approach for Local Interest Point Detection in Line-Drawing Images 1 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  Junction detection in line-drawing images  Experiments and results  Conclusion and future works 2

Introduction (1)  This work is interested with graphic documents, especially the line drawings, some examples 3

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: 4 Local interest points ApproachCornerJunctionRobustness High 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.

Overview  Introduction  Junction detection in line-drawing images  Experiments and results  Conclusion and future works 6

Junction detection in line-drawing images (1) 7 Flow-work of our approach

8 Junction detection in line-drawing images (2) Skeletonization, branch linking 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

9 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 graphA stroke pathA circuit path Skeletonization, branch linking Skeletonization, branch linking High curvature detection Path extraction 1D signals Skeleton graph Path representation 2D paths Refining & Correcting Candidates d0d0 are branch pixels are branch extremities is a crossing pixel d 0 is the extremity-crossing direction

Junction detection in line-drawing images (4) Skeletonization, branch linking 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: (x 1 y 1 ), (x 2 y 2 ),…,(x N y N ) To represent a 2D path in 1D signal, we selected the Rosenfeld- Johnston method: 10 p i-q pipi p i+q  p I-q pIpI p I+q  f(t) straight-line  high curvature  /2 0 p I-q pIpI p I+q

Junction detection in line-drawing images (5) Skeletonization, branch linking 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)  A good starting point here (shift-invariant). Not good starting point.

12 Skeletonization, branch linking 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 representation Image (I) 2D curcuit path Multi-resolution wavelet analysis

 (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. 13 Junction detection in line-drawing images (7) Skeletonization, branch linking 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

Overview  Introduction  Junction detection in line-drawing images  Experiments and results  Conclusion and future works 14

Experiments and Results (1)  Evaluation protocol: 15 Evaluation Criteria is the repeatability score [Schmid00] 2  p is a model point q is a detected point p q Detection of p is positive if d(p,q)<  with d(p,q) the Euclidean distance

Experiments and Results (2) 16  Datasets: Logos-UMDISRC2011 Models DegradationRotation + Scaling + Kanungo noise Test images

Experiments and Results (3) 17  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).

Experiments and Results (4) 18  Some visual results

Overview  Introduction  Junction detection in line-drawing images  Experiments and results  Conclusion and future works 19

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 20

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