A Robust Approach for Local Interest Point Detection in Line-Drawing Images

1. 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 7th March 2012 Athens, Greece

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

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

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: Comparison of the approaches for corner and junction detection Local interest points

5. Introduction (3) • 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. • 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

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

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

8. Junction detection in line-drawing images (2) • (1) Skeletonization based on Di Baja (3,4)-chamfer distance [DiBaja94] • (2) Branch linking and Skeleton Connective Graph Construction (SCG) based on [Popel02] Skeletonization, branch linking Skeleton graph Path extraction 2D paths Path representation 1D signals High curvature detection • Skeleton Connective Graph (SCG): • node: ended and crossing points • edge: skeleton branch Candidates Refining & Correcting

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: Skeletonization, branch linking Skeleton graph Path extraction 2D paths Path representation 1D signals High curvature detection A skeleton graph A stroke path A circuit path Candidates are branch pixels d0 are branch extremities Refining & Correcting is a crossing pixel d0 is the extremity-crossing direction

10. Junction detection in line-drawing images (4) • 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: Skeletonization, branch linking pi Skeleton graph pi+q  Path extraction pi-q 2D paths Path representation 1D signals High curvature detection Candidates pI-q pI pI+q Refining & Correcting pI-q pI pI+q

11. Junction detection in line-drawing images (5) • 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 Skeletonization, branch linking Skeleton graph Path extraction A good starting point here (shift-invariant). Not good starting point. 2D paths Path representation 1D signals High curvature detection Candidates Refining & Correcting

12. Junction detection in line-drawing images (6) • Using multi-resolution wavelet analysis because of its robustness and scale invariance (i.e. multi-resolution)[Gao06]. Skeletonization, branch linking Skeleton graph 2D curcuit path Image (I) 1D representation Path extraction 2D paths Path representation Multi-resolution wavelet analysis 1D signals High curvature detection Candidates Refining & Correcting

13. Junction detection in line-drawing images (7) • (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. Skeletonization, branch linking Skeleton graph Path extraction 2D paths Path representation a SCG with high curvature points a path with high curvature points result after removing short segments 1D signals High curvature detection Candidates Refining & Correcting

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

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

16. Experiments and Results (2) • Datasets:

17. 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).

18. Experiments and Results (4) • Some visual results

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

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

21. Thank you for your attention!