Abstract
Gradient calculation and edge detection are well-known problems in image processing and the fundament for many approaches for line detection, segmentation, contour extraction, or model fitting. A large variety of algorithms for edge detection already exists but strong image noise is still a challenge. Especially in automatic surveillance and reconnaissance applications with visual-optical, infrared, or SAR imagery, high distance to objects and weak signal-to-noise-ratio are difficult tasks to handle. In this paper, a new approach using Local Binary Patterns (LBPs) is presented, which is a crossover between texture analysis and edge detection. It shows similar results as the Canny edge detector under normal conditions but performs better in presence of noise. This characteristic is evaluated quantitatively with different artificially generated types and levels of noise in synthetic and natural images.
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References
Canny, J.: Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Korn, A.: Toward a Symbolic Representation of Intensity Changes in Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 610–625 (1988)
Kitanovski, V., Taskovski, D., Panovski, L.: Multi-scale Edge Detection Using Undecimated Wavelet Transform. In: Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, ISSPIT (2008)
Agaian, S., Almuntashri, A.: Noise-Resilient Edge Detection Algorithm for Brain MRI Images. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC (2009)
Sun, X., Sun, G.: A New Noise-resistant Algorithm for Edge Detection. In: Proceedings of the Second International Workshop on Education Technology and Computer Science, ETCS (2010)
Panetta, K.A., Agaian, S.S., Nercessian, S.C., Almunstashri, A.A.: Shape-dependent canny edge detector. Optical Engineering 50 (2011)
Abdou, I.E., Pratt, W.K.: Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE 67, 753–763 (1979)
Chen, Y., Das, M.: Robust edge and corner detection using noise identification and adaptive thresholding techniques. In: Proceedings of the IEEE International Conference on Electro/Information Technology (2007)
Hou, Z.J., Wei, G.W.: A new approach to edge detection. Pattern Recognition 35, 1559–1570 (2002)
Chang, C.Y.: Contextual-based Hopfield neural network for medical image edge detection. Optical Engineering 45 (2006)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Guo, Z., Zhang, L., Zhang, D.: A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing 19, 1657–1663 (2010)
An, K.H., Park, S.H., Chung, Y.S., Moon, K.Y., Chung, M.J.: Learning discriminative multi-scale and multi-position LBP features for face detection based on Ada-LDA. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1117–1122 (2009)
Heikkilä, M., Pietikäinen, M.: A Texture-Based Method for Modeling the Background and Detecting Moving Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 657–662 (2006)
Teutsch, M., Saur, G.: Segmentation and Classification of Man-Made Maritime Objects in TerraSAR-X Images. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS (2011)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of Interest Regions with Local Binary Patterns. Pattern Recognition 42, 425–436 (2009)
Mäenpää, T.: The Local Binary Pattern Approach to Texture Analysis - Extensions and Applications. Dissertation, University of Oulu, Finland (2003)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
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Teutsch, M., Beyerer, J. (2013). Noise Resistant Gradient Calculation and Edge Detection Using Local Binary Patterns. 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_1
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DOI: https://doi.org/10.1007/978-3-642-37410-4_1
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