Slide 1

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- PolytechTour, France. CIL Talk Wednesday 7 th March 2012 Athens, Greece

Slide 2

Overview Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works 2

Slide 3

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

Slide 4

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

Slide 5

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.

Slide 6

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

Slide 7

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

Slide 8

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

Slide 9

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

Slide 10

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

Slide 11

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) -1 11 A good starting point here (shift-invariant). Not good starting point.

Slide 12

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

Slide 13

(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

Slide 14

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

Slide 15

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

Slide 16

Experiments and Results (2) 16 Datasets: Logos-UMDISRC2011 Models106150 DegradationRotation + Scaling + Kanungo noise Test images12723600

Slide 17

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).

Slide 18

Experiments and Results (4) 18 Some visual results

Slide 19

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

Slide 20

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

Slide 21

Thank you for your attention!