Abstract
Boundary detection is essential for a variety of computer vision tasks such as segmentation and recognition. We propose a unified formulation for boundary detection, with closed-form solution, which is applicable to the localization of different types of boundaries, such as intensity edges and occlusion boundaries from video and RGB-D cameras. Our algorithm simultaneously combines low- and mid-level image representations, in a single eigenvalue problem, and we solve over an infinite set of putative boundary orientations. Moreover, our method achieves state of the art results at a significantly lower computational cost than current methods. We also propose a novel method for soft-segmentation that can be used in conjunction with our boundary detection algorithm and improve its accuracy at a negligible extra computational cost.
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References
Roberts, L.: Machine perception of three-dimensional solids. In: Optical and Electro-Optical Information Processing, pp. 159–197. MIT Press (1965)
Prewitt, J.: Object enhancement and extraction. In: Picture Processing and Psychopictorics, pp. 75–149. Academic Press, New York (1970)
Marr, D., Hildtreth, E.: Theory of edge detection. Proc. Royal Society (1980)
Canny, J.: A computational approach to edge detection. PAMI 8, 679–698 (1986)
Ruzon, M., Tomasi, C.: Edge, junction, and corner detection using color distributions. PAMI 23 (2001)
Stein, A., Hebert, M.: Occlusion boundaries from motion: Low-level detection and mid-level reasoning. IJCV 82 (2009)
Sundberg, P., Brox, T., Maire, M., Arbelaez, P., Malik, J.: Occlusion boundary detection and figure/ground assignment from optical flow. In: CVPR (2011)
He, X., Yuille, A.: Occlusion Boundary Detection Using Pseudo-depth. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 539–552. Springer, Heidelberg (2010)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33 (2011)
Mairal, J., Leordeanu, M., Bach, F., Hebert, M., Ponce, J.: Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 43–56. Springer, Heidelberg (2008)
Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV (2011)
Kanade, T.: Image understanding research at CMU. In: DARPA IUW (1987)
Di Senzo, S.: A note on the gradient of a multi-image. CVGIP 33 (1986)
Cumani, A.: Edge detection in multispectral images. CVGIP 53 (1991)
Koschan, M., Abidi, M.: Detection and classification of edges in color images. Signal Processing Magazine, Special Issue on Color Image Processing 22 (2005)
Martin, D., Fawlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26 (2004)
Ren, X.: Multi-scale Improves Boundary Detection in Natural Images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 533–545. Springer, Heidelberg (2008)
Meer, P., Georgescu, B.: Edge detection with embedded confidence. PAMI 23 (2001)
Baker, S., Nayar, S.K., Murase, H.: Parametric feature detection. In: DARPA Image Understanding Workshop (1997)
Petrou, M., Kittler, J.: Optimal edge detectors for ramp edges. PAMI 13 (1991)
Lagarias, J., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Optimization 9 (1998)
Catanzaro, B., Su, B.Y., Sundaram, N., Lee, Y., Murphy, M., Keutzer, K.: Efficient, high-quality image contour detection. In: ICCV (2009)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22 (2000)
Sargin, M., Bertelli, L., Manjunath, B., Rose, K.: Probabilistic occlusion boundary detection on spatio-temporal lattices. In: ICCV (2009)
Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: CVPR (2010)
Leordeanu, M., Sukthankar, R., Sminchisescu, C.: Generalized boundaries from multiple image interpretations. Techincal Report, Institute of Mathematics of the Romanian Academy (August 2012)
Sun, D., Roth, S., Black, M.: Secrets of optical flow estimation and their principles. In: CVPR (2010)
Brox, T., Bregler, C., Malik, J.: Large displacement optical flow. In: CVPR (2009)
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Leordeanu, M., Sukthankar, R., Sminchisescu, C. (2012). Efficient Closed-Form Solution to Generalized Boundary Detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33765-9_37
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DOI: https://doi.org/10.1007/978-3-642-33765-9_37
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