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Abstrakty
At the planning and construction of new infrastructures, the information about migration potential of animals in a target area is needed. This information will be used to design of migration corridors for wild animals. To determine the migration potential of animals based on distributed video camera system, new methods for object recognition and classification are developed. In general, an object recognition system consists of three steps, namely, the image feature extraction from the training database, training the classifier and evaluation of query image of object/animal. In this paper, an extraction of local key point by SIFT or SURF descriptors, bags of key points method in combination with SVM classifier and two hybrid key points detection methods are proposed in detail.
Czasopismo
Rocznik
Tom
Strony
26--30
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
autor
- University of Zilina, Department of Telecommunication and Multimedia, Zilina, Slovakia
autor
- University of Zilina, Department of Telecommunication and Multimedia, Zilina, Slovakia
autor
- University of Zilina, Department of Telecommunication and Multimedia, Zilina, Slovakia
autor
- University of Zilina, Department of Telecommunication and Multimedia, Zilina, Slovakia
Bibliografia
- [1] SONKA M., HLAVAC V., BOYLE R.: Image Processing, Analysis and Machine Vision, Thomson Learning, part of the Thompson Corporation, ISBN: 10: 0-495-24438-4, 2008
- [2] CAO. T.C.: Object Recognition. ISBN 978-953-307-222-7 2011
- [3] HOSSAIN K., PAREKH R.: Extending GLCM to include Color Information for Texture Recognition, International Conference on Modeling, Optimization and Computing, AIP Conference Proceedings, Volume: 1298, Pages: 583-588, 2010
- [4] NGAN K.N., LI H.: Video Segmentation and Its Applications. Springer Science+Business Media 2011, ISBN 978-1441994813.
- [5] NIXON M., AGUANDO A.: Feature extraction & image processing, second edition, ISBN 978-0-1237-2538-7, 2008
- [6] UIJLINGS, J. R. R., SMEULDERS, A. W. M., SCHA, R. J. H.: The Visual Extent of an Object, International Journal of Computer Vision, Volume: 96, Pages: 46-63, Jan 2012
- [7] BAY T., TUYTELAARS T., LUC V.G.: SURF, Speeded Up Robust Features, ETH Zurich, Preprint submitted to Elsevier, 10 September 2008
- [8] KOEN E. A., GEVERS T., CEES G.M.: Cees; Color Descriptors for object category recognition, Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:32 , Issue: 9 ), Sept. 2010, pages 1582–1596, ISSN: 0162-8828
- [9] ZHANG J., MARSZALEK M.: Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study, Journal of Computer Vision, 2006 Springer Science + Business Media, LLC. Manufactured in the United States
- [10] NABER M., HILGER M., EINHAUSER W.: Animal detection and identification in natural scenes: Image statistics and emotional valence, Journal of Vision, Volume: 12, 2012
- [11] GROSSBERG S., MARKOWITZ J., CAO, YQ.: On the road to invariant recognition: Explaining tradeoff and morph properties of cells in inferotemporal cortex using multiplescale task-sensitive attentive learning, Neural Networks, Volume: 24, Pages: 1036-1049, 2011
- [12] Csurka G., Dance Ch. R., Fan L., Willamowski J., Bray C.: Visual Categorization with Bags of Keypoints, Xerox Research Centre Europe, France 2001.
- [13] Shukran M. A. M., Chung Y.Y, Yeh W. Ch., Wahid N.: “Image Classification Technique using Modified Particle Swarm Optimization,” Modern Applied Science, Vol. 5, No. 5; October 2011
- [14] Supichai T.: “Image Classification,” Available at: http://www.sc.chula.ac.th/courseware/2309507/Lecture/remote18.htm, accessed 18.08.2013.
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