Abstract
The majority of vision research focusses on advancing technical methods for image analysis, with a coupled increase in complexity and sophistication. The problem of providing access to these sophisticated techniques is largely ignored, leading to a lack of application by mainstream applications. We present a feature-based clustering segmentation algorithm with novel modifications to fit a developer-centred abstraction. This abstraction acts as an interface which accepts a description of segmentation in terms of properties (colour, intensity, texture, etc.), constraints (size, quantity) and priorities (biasing a segmentation). This paper discusses the modifications needed to fit the algorithm into the abstraction, which conditions of the abstraction it supports, and results of the various conditions demonstrating the coverage of the segmentation problem space. The algorithm modification process is discussed generally to help other researchers mould their algorithms to similar abstractions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shreiner, D., Woo, M., Neider, J., Davis, T.: OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2, 5th edn. Addison-Wesley Professional (2005)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. O’Reilly Media, Inc. (2008)
Shaw, K.B., Lohrenz, M.C.: A survey of digital image segmentation algorithms. Final Technical Report ADA499374, Naval Oceanographic and Atmospheric Research Lab (1995)
Skarbek, W., Koschan, A.: Colour image segmentation - a survey. Technical report, Institute for Technical Informatics, Technical University of Berlin (1994)
Chan, T., Sandberg, B., Moelich, M.: Some recent developments in variational image segmentation. In: Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, pp. 175–210. Springer (2005)
Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding 110, 260–280 (2008)
Raut, S., Raghuvanshi, M., Dharaskar, R., Raut, A.: Image segmentation: A state-of-art survey for prediction. In: Proceedings of International Conference on Advanced Computer Control, pp. 420–424. IEEE Computer Society, New York (2009)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)
Lucchese, L., Mitra, S.K.: Advances in color image segmentation. In: Proceedings of Global Telecommunications Conference, pp. 2038–2044. IEEE Computer Society, Berkeley (1999)
Bow, S.T.: Pattern Recognition and Image Preprocessing, 2nd edn. CRC Press (2002)
Pavlidis, T., Liow, Y.T.: Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 225–233 (1990)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae-Special issue on mathematical morphology 41, 187–228 (2000)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)
Eumt, K.-B., Lee, J., Venetsanopoulos, A.N.: Color image segmentation using a possibilistic approach. In: IEEE International Conference on Systems, Man, and Cybernetics - SMC, vol. 2, pp. 1150–1155. IEEE Computer Society, New York (1996)
Comaniciu, D., Meer, P.: Robust analysis of feature spaces: Color image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750–755. IEEE Computer Society, New York (1997)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Wang, W.: Color image segmentation and understanding through connected components. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1089–1093. IEEE Computer Society, New York (1997)
Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. Transactions on Pattern Analysis and Machine Intelligence 10, 579–586 (1988)
Cardoso, J., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Transactions on Image Processing 14, 1773–1782 (2005)
Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based-reasoning for image segmentation. Pattern Recognition and Artificial Intelligence 22, 829–842 (2008)
Yong, X., Feng, D., Rongchun, Z., Petrou, M.: Learning-based algorithm selection for image segmentation. Pattern Recognition Letters 26, 1059–1068 (2005)
Martin, V., Maillot, N., Thonnat, M.: A learning approach for adaptive image segmentation. In: Proceedings of the Fourth IEEE International Conference on Computer Vision Systems (ICVS 2006). IEEE Computer Society (2006)
Nickisch, H., Kohli, P., Rother, C., Rhemann, C.: Learning an interactive segmentation system. In: Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing, pp. 274–281. ACM, New York (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jang, D., Miller, G., Fels, S. (2013). Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-37410-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37409-8
Online ISBN: 978-3-642-37410-4
eBook Packages: Computer ScienceComputer Science (R0)