Abstract
In this paper we present a new method for obtaining a list of interest objects from a single image. Our object extraction method works on two well known algorithms: the Canny edge detection method and the quadrilaterals detection. Our approach allows to select only the significant elements of the image. In addition, this method allows to filter out unnecessary key points in a simple way (for example obtained by the SIFT algorithm) from the background image. The effectiveness of the method is confirmed by experimental research.
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
Barghout, L., Sheynin, J.: Real-world scene perception and perceptual organization: Lessons from computer vision. Journal of Vision 13(9), 709 (2013)
Bartczuk, L., Przybyl, A., Dziwinski, P.: Hybrid state variables - fuzzy logic modelling of nonlinear objects. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 227–234. Springer, Heidelberg (2013)
Batenburg, K., Sijbers, J.: Optimal threshold selection for tomogram segmentation by projection distance minimization. IEEE Transactions on Medical Imaging 28(5), 676–686 (2009)
Bazarganigilani, M.: Optimized image feature selection using pairwise classifiers. Journal of Artificial Intelligence and Soft Computing Research 1(2), 147–153 (2011)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Chang, Y., Wang, Y., Chen, C., Ricanek, K.: Improved image-based automatic gender classification by feature selection. Journal of Artificial Intelligence and Soft Computing Research 1(3), 241–253 (2011)
Cpalka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Transactions on Neural Networks 20(4), 701–714 (2009)
Cpalka, K., Rutkowski, L.: Flexible takagi sugeno neuro-fuzzy structures for nonlinear approximation. WSEAS Transactions on Systems 4(9), 1450–1458 (2005)
Damiand, G., Resch, P.: Split-and-merge algorithms defined on topological maps for 3d image segmentation. Graphical Models 65(1), 149–167 (2003)
Duda, P., Jaworski, M., Pietruczuk, L., Scherer, R., Korytkowski, M., Gabryel, M.: On the application of fourier series density estimation for image classification based on feature description. In: Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems, Krakow, Poland, November 7-9, pp. 81–91 (2013)
Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L.: Object detection by simple fuzzy classifiers generated by boosting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 540–547. Springer, Heidelberg (2013)
Gabryel, M., Nowicki, R.K., Woźniak, M., Kempa, W.M.: Genetic cost optimization of the GI/M/1/N finite-buffer queue with a single vacation policy. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 12–23. Springer, Heidelberg (2013)
Gabryel, M., Woźniak, M., Nowicki, R.K.: Creating learning sets for control systems using an evolutionary method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) SIDE 2012 and EC 2012. LNCS, vol. 7269, pp. 206–213. Springer, Heidelberg (2012)
Greblicki, W., Rutkowska, D., Rutkowski, L.: An orthogonal series estimate of time-varying regression. Annals of the Institute of Statistical Mathematics 35(1), 215–228 (1983)
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by data clustering and inverse document frequency. In: Mrozek, S.K.D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 374–383. Springer, Heidelberg (2014)
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Romanowski, J.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Parallel Processing and Applied Mathematics 2013. LNCS. Springer, Heidelberg (2014) (manuscript accepted for publication)
Kirillov, A.: Detecting some simple shapes in images (2010), http://www.aforgenet.com
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Nowicki, R.: Rough-neuro-fuzzy system with micog defuzzification. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 1958–1965 (2006)
Nowicki, R.: On classification with missing data using rough-neuro-fuzzy systems. International Journal of Applied Mathematics and Computer Science 20(1), 55–67 (2010)
Nowicki, R., Rutkowski, L.: Soft techniques for bayesian classification. In: Neural Networks and Soft Computing, pp. 537–544. Springer (2003)
Peteiro-Barral, D., Guijarro-Bardinas, B., Perez-Sanchez, B.: Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms. Journal of Artificial Intelligence and Soft Computing Research 2(1), 5–20 (2012)
Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 697–705. Springer, Heidelberg (2012)
Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Transactions on Circuits and Systems 33(8), 812–818 (1986)
Rutkowski, L.: Non-parametric learning algorithms in time-varying environments. Signal Processing 18(2), 129–137 (1989)
Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)
Sankaranarayanan, V., Lakshmi, S.: A study of edge detection techniques for segmentation computing approaches. IJCA, Special Issue on CASCT (1), 35–41 (2010)
Starczewski, J.T.: A type-1 approximation of interval type-2 FLS. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds.) WILF 2009. LNCS, vol. 5571, pp. 287–294. Springer, Heidelberg (2009)
Stockman, G., Shapiro, L.G.: Computer Vision, 1st edn. Prentice Hall PTR, Upper Saddle River (2001)
Tamaki, T., Yamamura, T., Ohnishi, N.: Image segmentation and object extraction based on geometric features of regions. In: Electronic Imaging 1999. International Society for Optics and Photonics, pp. 937–945 (1998)
Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 362–367. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S. (2014). From Single Image to List of Objects Based on Edge and Blob Detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-07176-3_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07175-6
Online ISBN: 978-3-319-07176-3
eBook Packages: Computer ScienceComputer Science (R0)