Левчук В. А., Ковалев В. А. МЕТОДИКА КОЛИЧЕСТВЕННОГО ОПИСАНИЯ БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ СЛОВАРЕЙ СУПЕРПИКСЕЛОВ. Информатика. 2016;(1):49-57.
1. Kovalev, V. Color Co-occurence Descriptors for Querying-by-Example / V. Kovalev, S. Volmer // Proc. of the 1998 Conf. on MultiMedia Modeling. – Switzerland, 1998. – P. 32–38.
2. Content-based image retrieval as a method for melanoma diagnosis / V. Liauchuk [et al.] // Proc. of the 29th Intern. Congress on Computer Assisted Radiology and Surgery (CARS– 2015). – Spain, 2015. – Vol. 10. – P. 291–292.
3. Pinto, N. A high-throughput screening approach to discovering good forms of biologically inspired visual representation / N. Pinto, D. Doukhan, D. Cox // PLoS Computational Biology. – 2009. – Vol. 5(11). – P. 1–12.
4. Evaluating Bag-of-Visual-Words Representations in Scene Classification / J. Yang [et al.] // Proc. of the Intern. Workshop on Multimedia Information Retrieval. – Germany, Augsburg, 2007. – P. 197–206.
5. Quantized Image Patches Co-occurrence Matrix: A New Statistical Approach for Texture Classification using Image Patch Exemplars / Zh. Liu [et al.] // Proc. of the 3th Intern. Conf. on Digital Image Processing (ICDIP 2011). – China, Chengdu, 2011. – Vol. 8009. – P. 56–64.
6. Fulkerson, B. Class segmentation and object localization with superpixel neighborhoods / B. Fulkerson, A. Vedaldi, S. Soatto // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Japan. – 2009. – P. 8.
7. Levinshtein, A. Multiscale symmetric part detection and grouping / A. Levinshtein, C. Sminchisescu, S. Dickinson // Proc. of the 12th Intern. Conf. on Computer Vision. – Kyoto, Sapan. – 2009. – Vol. 104. – P. 117–134.
8. Li, X. An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding / X. Li, Y. Guo // Proc. of the British Machine Vision Conference. – Surrey, 2012. – P. 1–11.
9. Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation / Q. Gao [et al.] // SPIE 9034, Medical Imaging 2014: Image Proc. – 2014. – Vol. 9034. – Р. 903407–903407-6.
10. Micusik, B. Semantic segmentation of street scenes by superpixel co-occurrence and 3D geometry / B. Micusik, J. Kosecka // IEEE Workshop on Video-Oriented Object and Event Classification (VOEC). – Japan, 2009. – P. 625–632.
11. He, X. Learning and incorporating top-down cues in image segmentation / X. He, R. Zemel, D. Ray // Proc. of the 9th European Conf. on Computer Vision. – Austria, 2006. – P. 338–351.
12. Sicre, R. SuperPixel based angular differences as a mid-level image descriptor / R. Sicre, E. Tasli, T. Gevers // Proc. of the 22th Intern. Conf. on Pattern Recognition (ICPR). – Sweden, 2014. – P. 3732–3737.
13. Content-based image retrieval of histology images / A. Dmitruk [et al.] // Proc. of Swedish Symposium on Image Analysis (SSBA-2010). – Uppsala, Sweden, 2010. – P. 91–92.
14. Tighe, J. SuperParsing: Scalable nonparametric image parsing with superpixels / J. Tighe, S. Lazebnik // ECCV’10 Proc. of the 11th European Conf. on Computer Vision. – Germany, Heidelberg, 2010. – P. 352–365.
15. SLIC Superpixels Compared to State-of-the-art Superpixel Methods / R. Achanta [et al.] // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2012. – Vol. 34(11). – P. 2274– 2282.
16. Ковалев, В.А. Влияние мер близости в пространстве признаков на качество поиска медицинских изображений по содержанию / В.А. Ковалев, А.А. Дмитрук // Информатика. – 2011. – № 30. – С. 5–11.
17. Efron, B. Bootstrap Methods: Another Look at the Jackknife / B. Efron // Annals of Statistics. – 1979. – Vol. 1. – P. 1–26.
18. Matas, T. Extended set of local binary patterns for rapid object detection / T. Matas, J. Matas // Proc. of the Computer Vision Winter Workshop. – Czech Republic, Nové Hrady, 2010. – Vol. 2010. – P. 37–43.
