Abstract
Intelligent computer vision systems can be based on various approaches, methods and algorithms. One of them is the usage of object descriptors, devoted to the representation of objects extracted from digital images (or video sequences) mainly by means of low-level features. There are several features that are applied in computer vision theory and practice, e.g. color, context of the information, luminance, movement, shape, and texture. Amongst them shape, color and texture are especially popular. To the contrary, object representation based on greyscale is less popular. The paper proposes and analyses a new simple and fast algorithm for greyscale object representation. The description method is based on the usage of the polar transform of pixels belonging to an object, and projections – vertical and horizontal. Apart from these operations, some additional steps are also applied in order to improve the efficiency of the developed approach, e.g. median and low-pass filtering. The properties of the proposed greyscale object representation algorithm are analyzed experimentally by means of an exemplary application from the computer vision domain. The ear images (taken from The West Pomeranian University of Technology Ear Database) are applied in the experiment. The obtained results constitute the basis for certain conclusions as well as the proposition of future plans and works on the problem.
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References
Frejlichowski, D.: An algorithm for binary contour objects representation and recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 537–546. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69812-8_53
Verma, M., Raman, B.: Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J. Vis. Commun. Image Represent. 32, 224–236 (2015)
Gbèhounou, S., Lecellier, F., Fernandez-Maloigne, C.: Evaluation of local and global descriptors for emotional impact recognition. J. Vis. Commun. Image Represent. 38, 276–283 (2016)
Paschalakis, S., Lee, P.: Pattern recognition in grey level images using moment based invariant features. In: The 7th International Conference on Image Processing and its Applications, pp. 245–249 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Chin, T.J., Suter, D., Wang, H.: Boosting histograms of descriptor distances for scalable multiclass specific scene recognition. Image Vis. Comput. 29(4), 241–250 (2011)
Castrillón-Santana, M., de Marsico, M., Nappi, M., Riccio, D.: MEG: texture operators for multi-expert gender classification. Comput. Vis. Image Underst. 156, 4–18 (2017)
Delibasis, K., Undrill, P.E., Cameron, G.G.: Designing texture filters with genetic algorithms: an application to medical images. Signal Process. 57(1), 19–33 (1997)
Nanni, L., Melucci, M.: Combination of projectors, standard texture descriptors and bag of features for classifying images. Neurocomputing 173, 1602–1614 (2016)
Dharmagunawardhana, C., Mahmoodi, S., Bennett, M., Niranjan, M.: Gaussian Markov random field based improved texture descriptor for image segmentation. Image Vis. Comput. 32(11), 884–895 (2014)
Aptoula, E., Lefèvre, S.: Morphological texture description of grey-scale and color images. Adv. Imaging Electron Phys. 169, 1–74 (2011)
Faraki, M., Harandi, M.T., Wiliem, A., Lovell, B.C.: Fisher tensors for classifying human epithelial cells. Pattern Recogn. 47(7), 2348–2359 (2014)
Nanni, L., Brahnam, S., Lumini, A.: A simple method for improving local binary patterns by considering non-uniform patterns. Pattern Recogn. 45(10), 3844–3852 (2012)
Florindo, J.B., Landini, G., Bruno, O.M.: Three-dimensional connectivity index for texture recognition. Pattern Recogn. Lett. 84, 239–244 (2016)
Florindo, J.B., Bruno, O.M.: Local fractal dimension and binary patterns in texture recognition. Pattern Recogn. Lett. 78, 22–27 (2016)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Frejlichowski, D.: Identification of erythrocyte types in greyscale MGG images for computer-assisted diagnosis. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 636–643. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_79
Hupkens, T.M., de Clippeleir, J.: Noise and intensity invariant moments. Pattern Recogn. Lett. 16(4), 371–376 (1995)
Frejlichowski, D., Tyszkiewicz, N.: The west pomeranian university of technology ear database – a tool for testing biometric algorithms. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 227–234. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13775-4_23
Frejlichowski, D.: Application of the polar-fourier greyscale descriptor to the problem of identification of persons based on ear images. In: Choraś, R. (ed.) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol. 102, pp. 5–12. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23154-4_1
Klęsk, P., Kapruziak, M., Olech, B.: Boosted classifiers for antitank mine detection in C-scans from ground-penetrating radar. In: Wiliński, A., El Fray, I., Pejaś, J. (eds.) Soft Computing in Computer and Information Science. AISC, vol. 342, pp. 11–25. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15147-2_2
Acknowledgements
The paper was supported by The National Science Centre, Poland under the grant no. 2017/01/X/ST7/00347, entitled “Popularization – by means of the presentation at the international scientific conference – of the greyscale descriptors applied for objects extracted from digital images”.
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Frejlichowski, D. (2018). A New Algorithm for Greyscale Objects Representation by Means of the Polar Transform and Vertical and Horizontal Projections. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_58
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