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P. Jiju and M. Ramanathan Department of Engineering Design Indian Institute of Technology Madras. A graph theoretic approach for the construction of concave hull in r 2. Outline. Introduction Related Works Algorithm Implementation & Results Conclusion References. Introduction.
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P. Jiju and M. Ramanathan Department of Engineering Design Indian Institute of Technology Madras A graph theoretic approach for the construction of concave hull in r2 Department of Engineering Design, IIT Madras
Outline • Introduction • Related Works • Algorithm • Implementation & Results • Conclusion • References
Introduction • Convex hull-minimal Area convex enclosure • Limitations • Region occupied by trees in a forest • Boundary of a city • Applications of non-convex shapes • GIS • Image processing • Reconstruction • Protein structure • Data classification
Related Works • Papers on concave hull • ω-concave hull algorithm[5] • K-nearest neighbor algorithm[4] • Swinging arm algorithm[3] • Concave hull[11] • Different shapes proposed for point sets • α-shape, A-shape, S-shape, r-shape, chi-shape[1,2,6,7]
Limitations • lacks a standard definition • non-unique • Depends on external parameter • Application specific χ –shape for different λp
Minimal Perimeter Simple Polygon • Concave hull of set of n points in plane is the minimal perimetersimple polygon which passes through all the n points • An algorithm based on Euclidean TSP • NP Complete Problem
Minimal Perimeter Simple Polygon • Asymmetric point set Vs Symmetric Point set L2 L4 L3 L1 Department of Engineering Design, IIT Madras
Path Improvement • Original path • Path after a local move
Implementation & Results • Used Concorde TSP solver-LKH Heuristic[8] • Point sets used were st70, krod100 and pr299 from TSPLIB
Implementation & Results-ST70 points Concave hull • Presence of holes • Perimeter Length Alpha hull(α=10)
Implementation & Results-KROD100 Concave hull 3. Enclosure 4. Connectedness Alpha hull(α=175)
Implementation & results-PR299 Points Concave hull 5. Points spanned 6. Uniqueness Alpha hull(α=150)
Conclusion & Future Work • An attempt to relate concave hull to minimum perimeter simple polygon. • Compared the concave hull with other shapes • The idea can be extended to 3-dimension • Some methodology to tackle symmetric point set
Reference [1].A. R. Chaudhuri, B. B. Chaudhuri, and S. K. Parui. A novel approach to computation of the shape of a dot pattern and extraction of its perceptual border. Comput. Vis. Image Underst., 68:257–275, December 1997. [2]. H. Edelsbrunner, D. Kirkpatrick, and R. Seidel. On the shape of a set of points in the plane. Information Theory, IEEE Transactions on, 29(4):551 – 559, jul 1983. [3]. A. Galton and M. Duckham. What is the region occupied by a set of points? In M. Raubal, H. Miller, A. Frank, and M. Goodchild, editors, Geographic Information Science, volume 4197 of Lecture Notes in Computer Science, pages 81–98. Springer Berlin / Heidelberg,2006. 10.1007/118639396. [4].A. J. C. Moreira and M. Y. Santos. Concave hull: A knearestneighbours approach for the computation of the region occupied by a set of points. In GRAPP (GM/R), pages 61–68, 2007. [5]. J. Xu, Y. Feng, Z. Zheng, and X. Qing. A concave hull algorithm for scattered data and its applications. In Image and Signal Processing (CISP), 2010 3rd International Congress on, volume 5, pages 2430 –2433, oct.2010.
Reference [6]. M. Melkemi and M. Djebali. Computing the shape of a planar points set. Pattern Recognition, 33(9):1423 –1436, 2000. [7]. M. Duckham, L. Kulik, M. Worboys, and A. Galton.Efficient generation of simple polygons for characterizingthe shape of a set of points in the plane. Pattern Recogn., 41:3224–3236, October 2008. [8]. D. Karapetyan and G. Gutin. Lin-Kernighan Heuristic Adaptations for the Generalized Traveling Salesman Problem. ArXiv e-prints, Mar. 2010. [9]. K. Helsgaun. An effective implementation of the linkernighan traveling salesman heuristic. European Journal of Operational Research, 126:106–130, 2000. [10]. Jin-Seo Park and Se-Jong Oh, A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets, Journal of Information Science and Engineering, 2011.