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This study focuses on detecting interest points in graphic documents, particularly line drawings, emphasizing junction detection. The methodology involves high curvature detection for achieving junction detection using skeletonization, branch linking, and graph construction. The approach extracts paths from Skeleton Connective Graph and refines candidates for improved results.
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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
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
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
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
Overview • Introduction • Junction detection in line-drawing images • Experiments and results • Conclusion and future works
Junction detection in line-drawing images (1) Flow-work of our approach
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
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
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
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
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
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
Overview • Introduction • Junction detection in line-drawing images • Experiments and results • Conclusion and future works
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
Experiments and Results (2) • Datasets:
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).
Experiments and Results (4) • Some visual results
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