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New image descriptor from edge detector and blob extractor

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.
Rocznik
Strony
31--39
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Institute of Computational Intelligence, Czestochowa University of Technology Częstochowa, Poland
autor
  • Institute of Computational Intelligence, Czestochowa University of Technology Częstochowa, Poland
autor
  • Institute of Computational Intelligence, Czestochowa University of Technology Częstochowa, Poland
Bibliografia
  • [1] Bazarganigilani M., Optimized image feature selection using pairwise classifiers, Journal of Artificial Intelligence and Soft Computing Research 2011, 1, 147-153.
  • [2] Drozda P., Sopyła K., Górecki P., Online crowdsource system supporting ground truth datasets creation, Computing 12th Conference, ICAISC2013, Zakopane, June 9-13, 2013, eds. L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, J.M. Zurada, 532-539.
  • [3] Meskaldji K., Boucherkha S., Chikhi S., Color quantization and its impact on color histogram based image retrieval accuracy, Networked Digital Technologies 2009, 515-517.
  • [4] Huang J., Kumar S.R., Mitra M., Zhu W.J., Zabih R., Image indexing using color correlograms, Computer Vision and Pattern Recognition 1997, 762-768.
  • [5] Śmietański J., Tadeusiewicz R., Łuczyńska E., Texture analysis in perfusion images of prostate cancer - A case study, International Journal of Applied Mathematics and Computer Science 2010, 20(1), 149-156.
  • [6] Veltkamp R.C., Hagedoorn M., State of the art in shape matching, [in:] Principles of Visual Information Retrieval, ed. M.S. Lew, Springer, London - Berlin - Heidelberg 2001, 87-119.
  • [7] Zalasiński M., Cpałka K., New approach for the on-line signature verification based on method of horizontal partitioning, Artificial Intelligence and Soft Computing 2013, 342-350.
  • [8] Grossmann A., Wavelet transforms and edge detection, Stochastic Processes in Physics and Engineering, Series Mathematics and Its Applications 1988, 42, 149-157.
  • [9] Mallat S., Wen Liang Hwang, Singularity detection and processing with wavelets, IEEE Transactions on Information Theory 1992, 38(2), 617-643.
  • [10] Krim H., Tucker D., Mallat S., Donoho D., On denoising and best signal representation, IEEE Transactions on Information Theory 1999, 45(7), 2225-2238.
  • [11] Mallat S.G., A theory for multiresolution signal decomposition: the wavelet representation, Pattern Analysis and Machine Intelligence, IEEE Transactions 1989, 11, 7, 674-693.
  • [12] Bay H., Tuytelaars T., Van Gool L., Surf: Speeded up robust features, Computer Vision-ECCV 2006, 404-417.
  • [13] Evans C., Notes on the OpenSURF Library, University of Bristol, Tech. Rep., 2009.
  • [14] Canny J., A computational approach to edge detection, Pattern Analysis and Machine Intelligence, IEEE Transactions 1986, 8(6), 679-698.
  • [15] Bao P., Zhang D., Wu X., Canny edge detection enhancement by scale multiplication, Pattern Analysis and Machine Intelligence 2005, 27(9), 1485-1490.
  • [16] Wang B., Fan S., An improved Canny edge detection algorithm, Computer Science and Engineering 2009, 1, 497-500.
  • [17] Luo Y., Duraiswami R., Canny edge detection on NVIDIA CUDA, Computer Vision and Pattern Recognition Workshops 2008, 1-8.
  • [18] Grycuk R., Gabryel M., Korytkowski M., Scherer R., Voloshynovskiy S., From single image to list of objects based on edge and blob detection, Artificial Intelligence and Soft Computing 2014, 8468, 605-615.
  • [19] Damiand G., Resch P., Split-and-merge algorithms defined on topological maps for 3D image segmentation, Graphical Models 2003, 65(1), 149-167.
  • [20] Grycuk R., Gabryel M., Korytkowski M., Romanowski J., Scherer R., Improved digital image segmentation based on stereo vision and mean shift algorithm, Parallel Processing and Applied Mathematics 2014, 8384, 433-443.
  • [21] Grycuk R., Gabryel M., Korytkowski M., Scherer R. Content-based image indexing by data clustering and inverse document frequency, Beyond Databases, Architectures, and Structures 2014, 424, 374-383.
  • [22] Liu Y., Zhang D., Lu G., Ma W.Y., A survey of content-based image retrieval with high-level semantics, Pattern Recognition 2007, 40(1), 262-282.
  • [23] Kirillov A., Detecting some simple shapes in images, AForge .NET, 2010.
  • [24] Bay H., Speeded-up robust features (SURF), Computer Vision and Image Understanding 2008, 110, 3, 346-359.
  • [25] Terriberry T., French L., Helmsen J., GPU accelerating speeded-up robust features, Proceedings of 3DPVT 2008, 355-362.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11a2dade-b393-4ebf-9a20-0c1797845c7b
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