Abstract
Local patterns and other patch based features have been an integral part of various computer vision applications as they encode local structural and statistical information. In this paper, we propose an image coding technique that utilizes Zeckendorf representation of pixel intensities and basic mathematical operators such as intersection, set difference, maximum, summation etc. for summarization of image regions. The algorithm produces a Z-coded image that tells about the homogeneity or the contrast in image regions with all codes in a range of 0 to 255.
Keywords
V. Vigneron—This research was supported by the program Cátedras Franco-Brasileiras no Estado de São Paulo, an initiative of the French consulate and the state of São Paulo (Brazil). We thank our colleagues Prof. João M.T. Romano, Dr. Kenji Nose and Dr. Michele Costa, who provided insights that greatly assisted this work.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
This assumption is generally valid for a small number of values and the noise power (variance) is \(\frac{\varDelta ^2}{P-1}\), where \(P-1\) is the number of surrounding pixels.
References
Arbelaez, P.: Boundary extraction in natural images using ultrametric contour maps. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2006, New York, NY, USA, 17–22 June 2006, p. 182 (2006)
Delicato, L.S., Serrano-Pedraza, I., Suero, M., Derrington, A.M.: Two-dimensional pattern motion analysis uses local features. Vis. Res. 62, 84–92 (2012)
Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)
Pietikinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Computer Imaging and Vision, vol. 40. Springer, London (2011)
Syed, T.Q., Behlim, S.I., Merchant, A.K., Thomas, A., Khan, F.M.: Leveraging mutual information in local descriptions: from local binary patterns to the image. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9280, pp. 239–251. Springer, Cham (2015). doi:10.1007/978-3-319-23234-8_23
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75690-3_13
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994). doi:10.1007/BFb0028345
Zeckendorf, E.: Représentation des nombres naturels par une somme de nombres de fibonacci ou de nombres de lucas. Bull. Soc. Roy. Sci. Liege 41, 179–182 (1972)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Vigneron, V., Syed, T.Q., Duarte, L.T., Lang, E., Behlim, S.I., Tomé, AM. (2017). Z-Images. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-58838-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58837-7
Online ISBN: 978-3-319-58838-4
eBook Packages: Computer ScienceComputer Science (R0)