Abstract
The segmentation of cut surfaces from a stack of wood logs is a challenging task and leads to many problems. Wood logs theoretically have a certain shape and color, which is the main reason to apply object detection methods. But in real world images there are many disturbing factors, such as defects, dirt or non-elliptical logs. In this paper we mainly address the problem of wood and wood log segmentation by combining object detection with a graph-cut segmentation. We introduce an iterative segmentation procedure, which detects the stack of wood, segments foreground and background, and separates the logs. Our novel approach works fully automatically and has no restrictions on the image acquisition other than well visible log cut surfaces. All three steps of our approach are novel and could be applied on similar problems. We implemented and evaluated different methods and show that of these approaches, our methods leads to the best results.
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References
Dahl, A.B., Guo, M., Madsen, K.H.: Scale-space and watershed segmentation for detection of wood logs. In: Vision Day, Informatics and Mathematical Modelling (2006)
Fink, F.: Foto-optische erfassung der dimension von nadelrundholzabschnitten unter einsatz digitaler bildverarbeitender methoden. Dissertation, Fakultaet fuer Forst- und Umweltwissenschaften der Albert-Ludwigs-Universitaet Freiburg i. Brsg (2004)
Gutzeit, E., Ohl, S., Voskamp, J., Kuijper, A., Urban, B.: Automatic wood log segmentation using graph cuts. In: Richard, P., Braz, J. (eds.) VISIGRAPP 2010. CCIS, vol. 229, pp. 96–109. Springer, Heidelberg (2011)
Jaehne, B.: Digital Image Processing, 6th reviewed and extended edn. Springer, Heidelberg (2005)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary region segmentation of object in n-d images. In: Int. C. Comput. Vision, pp. 105–112 (2001)
Rothar, C., Kolmogorov, V., Blake, A.: Grabcut - interactive forground extraction using iterated graph cuts. In: ACM Transactions on Graphics, pp. 309–314. ACM Press (2004)
Orchard, M., Bouman, C.: Color quantization of images. IEEE Transactions on Signal Processing, 2677–2690 (1991)
Couprie, M., Najman, L., Bertrand, G.: Quasi-linear algorithms for the topological watershed. Journal of Mathematical Imaging and Vision 22, 231–249 (2005)
Zhang, J., Oe, S.: A segmentation method of texture image by using pyramid linking and neural networks. In: Proceedings of the 36th SICE Annual Conference, SICE 1997. International Session Papers, pp. 1267–1272 (1997)
Le, T.V., Kulikowski, C.A., Muchnik, I.B.: A Graph-Based Approach for Image Segmentation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 278–287. Springer, Heidelberg (2008)
Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley (2009)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2001)
Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 53–56 (1995)
Chen, Q., Yang, X., Petriu, E.M.: Watershed segmentation for binary images with different distance transforms. In: IEEE International Workshop on Haptic, Audio and Visual Environments, HAVE 2004 (2004)
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Gutzeit, E., Voskamp, J. (2012). Automatic Segmentation of Wood Logs by Combining Detection and Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_25
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DOI: https://doi.org/10.1007/978-3-642-33179-4_25
Publisher Name: Springer, Berlin, Heidelberg
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