Using local binary patterns for object detection in images
Article Sidebar
Main Article Content
Abstract
The article discusses a texture operator called Local Binary Patterns (LBP) and its applications in image processing and object detection. We provide a description of the algorithm for computing LBP together with a rationale for using LBP as a feature for object detection and image recognition. Based on the algorithm we show that LBP features have a low computational overhead compared to more complicated image features such as the commonly used SIFT or SURF features or neural network based approaches because they exploit the use of extremely fast bitwise and integer operations of the CPU. We demonstrate that LBP is robust to changes in brightness, contrast, image rotation, image scale. We develop two enhancements for LBP that improve its resistance to camera noise and enhance the discriminative power of LBP when it is used as a feature for machine learning algorithms. We present the results on a challenging real-world object detection task.
Â
Keywords: computer vision, object detection, local binary patterns.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Global Journal of Computer Sciences: Theory and Research is an Open Access Journal. All articles can be downloaded free of charge. Articles published in the Journal are Open-Access articles distributed under CC-BY license [Attribution 4.0 International (CC BY 4.0)]
Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi (BD-Center) is a gold open access publisher. At the point of publication, all articles from our portfolio of journals are immediately and permanently accessible online free of charge. BD-Center articles are published under the CC-BY license [Attribution 4.0 International (CC BY 4.0)], which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and the source are credited.
References
[2] Xu, Q., Yang, J., & Ding, S. (2005). Texture segmentation using LBP embedded region competition. Electronic Letters on Computer Vision and Image Analysis, 5(1), 41-47.
[3] Rara, H., Farag, A., Elhabian, S., Ali, A., Miller, W., Starr, T., & Davis, T. (2010, September). Face recognition at-a-distance using texture and sparse-stereo reconstruction. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1-6). IEEE.
[4] Hadfield, S., & Bowden, R. (2012). Generalised pose estimation using depth. InTrends and Topics in Computer Vision (pp. 312-325). Springer Berlin Heidelberg.
[5] Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(12), 2037-2041.
[6] Wang, X., Han, T. X., & Yan, S. (2009, September). An HOG-LBP human detector with partial occlusion handling. In Computer Vision, 2009 IEEE 12th International Conference on (pp. 32-39). IEEE.
[7] Trefný, J., & Matas, J. (2010). Extended set of local binary patterns for rapid object detection. In Proceedings of the Computer Vision Winter Workshop (Vol. 2010).
[8] Chang, T., & Kuo, C. J. (1993). Texture analysis and classification with tree-structured wavelet transform. Image Processing, IEEE Transactions on, 2(4), 429-441.
[9] Livens, S., Scheunders, P., Van de Wouwer, G., & Van Dyck, D. (1997, July). Wavelets for texture analysis, an overview. In Image Processing and Its Applications, 1997., Sixth International Conference on (Vol. 2, pp. 581-585). IET.
[10] Hsu, T., Calway, A. D., & Wilson, R. (1993, May). Texture analysis using the multiresolution Fourier transform. In 8th Scandinavian Conference on Image Analysis.
[11] Ojala, T., Pietikäinen, M., & Mäenpää, T. (2000). Gray scale and rotation invariant texture classification with local binary patterns. In Computer Vision-ECCV 2000 (pp. 404-420). Springer Berlin Heidelberg.
[12] Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), 971-987.
[13] Pietikäinen, M., Ojala, T., & Xu, Z. (2000). Rotation-invariant texture classification using feature distributions. Pattern Recognition, 33(1), 43-52.
[14] Guo, Z., Zhang, L., Zhang, D., & Mou, X. (2010, September). Hierarchical multiscale LBP for face and palmprint recognition. In Image Processing (ICIP), 2010 17th IEEE International Conference on (pp. 4521-4524). IEEE.
[15] “OpenCV,†OpenCV. [Online]. Received February 20, 2014 from: http://www.opencv.org.