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[58] Guoshen Yu and Jean-Michel Morel. A fully affine invariant image comparison method. 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1597–1600, 2009. [59] Barry N. Taylor and Chris E. Kuyatt. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. NIST technical note 1297, United States Department of Commerce, Technology Administration, National Institute of Standards and Technology, September 1994. [60] Zheshen Wang and Baoxin Li. A two-stage approach to saliency detection in images. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pages 965–968, April 2008. [61] Timor Kadir and Michael Brady. Saliency, scale and image description. International Journal of Computer Vision, 45(2):83–105, November 2001. [62] Timor Kadir, Djamal Boukerroui, and Michael Brady. An analysis of the Scale Saliency algorithm. Technical report, ouel no: 2264/03, University of Oxford, 2003. [63] Stephen M. Smith and J. Michael Brady. SUSAN - a new approach to low level image processing. Technical report TR95SMS1c, Defence Research Agency, Farnborough, Hampshire, UK, 1995. [64] Stephen M. Smith and J. Michael Brady. SUSAN - a new approach to low level image processing. International Journal of Computer Vision, 23(1):45–78, May 1997. [65] Stephen M. Smith. Edge thinning used in the SUSAN edge detector. Technical report TR95SMS5, Defence Research Agency, Farnborough, Hampshire, UK, 1995. [66] Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Mark C. Benfield, and Erik G. Learned-Miller. Combining local and global image features for object class recognition. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Workshops, CVPR ’05, Washington, DC, USA, June 2005. IEEE Computer Society. [67] Aude Oliva and Antonio Torralba. Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research, 155:23–36, 2006. [68] Antonio Torralba. How many pixels make an image? Visual Neuroscience, 26(1):123– 131, 2009. [69] Chad Carson, Serge Belongie, Hayit Greenspan, and Jitendra Malik. Blobworld: Image segmentation using expectation-maximi<strong>za</strong>tion and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8):1026–1038, August 2002. [70] Li Fei-Fei and Pietro Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ’05, pages 524–531, Washington, DC, USA, 2005. IEEE Computer Society. 38
[71] Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR ’06, pages 2169–2178, 2006. [72] Tinne Tuytelaars. Dense interest points. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2281–2288, 2010. [73] Raphaël Marée, Pierre Geurts, Justus Piater, and Louis Wehenkel. Random subwindows for robust image classification. In International Conference on Computer Vision and Pattern Recognition (CVPR), pages 34–40, 2005. [74] Eric Nowak, Frédéric Jurie, and Bill Triggs. Sampling strategies for bag-of-features image classification. In European Conference on Computer Vision, ECCV ’06, pages 490–503. Springer, May 2006. [75] Anna Bosch, Xavier Muñoz, and Robert Martí. Review: Which is the best way to organize/classify images by content? Image and Vision Computing, 25(6):778–791, June 2007. 39
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Page 19 and 20: danas naziva Plessey detektor [44],
Page 21 and 22: gdje je: kutnost (engl. cornerness)
Page 23 and 24: Laplace operator je jako osjetljiv
Page 25 and 26: Slika 3.15: Lokalne značajke detek
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Page 29 and 30: Slika 3.19: Ilustracija važnosti g
Page 31 and 32: Slika 3.22: Primjer konstrukcije hi
Page 33 and 34: Popis oznaka i kratica 2D - 2-Dimen
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Page 37: [42] Theodosios Pavlidis. Algorithm

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