Abstract
The multiscale techniques for edge detection aim to combine the advantages of small and large scale methods, usually by blending their results. In this work we introduce a method for the multiscale extension of the Gravitational Edge Detector based on a t-norm T. We smoothen the image with a Gaussian filter at different scales then perform inter-scale edge tracking. Results are included illustrating the improvements resulting from the application of the multiscale approach in both a quantitative and a qualitative way.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Babaud, J., Witkin, A.P., Baudin, M., Duda, R.O.: Uniqueness of the gaussian kernel for scale-space filtering. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(1), 26–33 (1986)
Baddeley, A.J.: Errors in binary images and an L p version of the Hausdorff metric. Nieuw Archief voor Wiskunde 10, 157–183 (1992)
Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Carlotto, M.J.: Histogram analysis using a scale-space approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 9(1), 121–129 (1987)
Coleman, S., Scotney, B., Suganthan, S.: Multi-scale edge detection on range and intensity images. Pattern Recognition 44(4), 821–838 (2011)
Demigny, D.: On optimal linear filtering for edge detection. IEEE Trans. on Image Processing 11(7), 728–737 (2002)
Florack, L., Kuijper, A.: The topological structure of scale-space images. Journal of Mathematical Imaging and Vision 12, 65–79 (2000)
Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.: A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(12), 1338–1359 (1997)
Jackway, P., Deriche, M.: Scale-space properties of the multiscale morphological dilation-erosion. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(1), 38–51 (1996)
Konishi, S., Yuille, A., Coughlan, J.: A statistical approach to multi-scale edge detection. Image and Vision Computing 21(1), 37–48 (2003)
Leung, Y., Zhang, J.S., Xu, Z.B.: Clustering by scale-space filtering. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12), 1396–1410 (2000)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 117–156 (1998)
Lopez-Molina, C., Bustince, H., Fernandez, J., Couto, P., De Baets, B.: A gravitational approach to edge detection based on triangular norms. Pattern Recognition 43(11), 3730–3741 (2010)
Mallat, S., Hwang, W.: Singularity detection and processing with wavelets. IEEE Trans. on Information Theory 38(2), 617–643 (1992)
Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London 207(1167), 187–217 (1980)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)
McIlhagga, W.: The canny edge detector revisited. International Journal of Computer Vision 91, 251–261 (2011)
Peli, T., Malah, D.: A study of edge detection algorithms. Computer Graphics and Image Processing 20(1), 1–21 (1982)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Prewitt, J.M.S.: Object enhancement and extraction. In: Picture Processing and Psychopictorics, pp. 75–149. Academic Press (1970)
Qian, R., Huang, T.: Optimal edge detection in two-dimensional images. IEEE Trans. on Image Processing 5(7), 1215–1220 (1996)
Rosin, P.L.: Unimodal thresholding. Pattern Recognition 34(11), 2083–2096 (2001)
Russo, F.: FIRE operators for image processing. Fuzzy Sets and Systems 103(2), 265–275 (1999)
Shih, M.Y., Tseng, D.C.: A wavelet-based multiresolution edge detection and tracking. Image and Vision Computing 23(4), 441–451 (2005)
Sobel, I., Feldman, G.: A 3x3 isotropic gradient operator for image processing (1968); presented at a talk at the Stanford Artificial Intelligence Project
Sun, G., Liu, Q., Liu, Q., Ji, C., Li, X.: A novel approach for edge detection based on the theory of universal gravity. Pattern Recognition 40(10), 2766–2775 (2007)
Torre, V., Poggio, T.: On edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8, 147–163 (1984)
Weickert, J.: Anisotropic Diffusion in Image Processing. ECMI Series, Teubner-Verlag (1998)
Witkin, A.P.: Scale-Space Filtering. In: 8th Int. Joint Conf. Artificial Intelligence, Karlsruhe, vol. 2, pp. 1019–1022 (1983)
Yuille, A.L., Poggio, T.A.: Scaling theorems for zero crossings. IEEE Trans. on Pattern Analisys and Machine Intelligence 8, 15–25 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lopez-Molina, C., De Baets, B., Bustince, H., Barrenechea, E., Galar, M. (2011). Multiscale Extension of the Gravitational Approach to Edge Detection. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)