Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Konferencja
International Conference on Computer Analysis of Images and Patterns (9 ; 2001 ; Warszawa)
Języki publikacji
Abstrakty
This paper reports a color image segmentation method based on a seeded region growing technique (SRG) and guided by a saliency-based visual attention algorithm. Inspired by biological vision the purely data-driven model of visual attention is built around the feature, conspicuity and saliency maps. Using chromatic as well as unchromatic scene features, it, automatically, generates a set of regions-of-interest (ROIs), which represent the most visually-salient locations of the image. The automatically selected points are then used as seeds by the region growing algorithm to segment the conspicuous parts of the scene, using a color homogeneity criterion. A snakes-based technique is then used to improve the contours of the segmented regions. The results reported in this paper clearly shoe the effectiveness of the considered model of visual attention to detect the salient locations in color images.
Wydawca
Czasopismo
Rocznik
Tom
Strony
3--11
Opis fizyczny
Bibliogr. 17 poz.,Rys., wykr.,
Twórcy
autor
- University of Neuchatel, Neuchatel Switzerland
autor
- University of Neuchatel, Neuchatel Switzerland
autor
- University of Neuchatel, Neuchatel Switzerland
autor
- University of Neuchatel, Neuchatel Switzerland
Bibliografia
- [1] Itti L., Koch Cli., Niebnr E., A model of saliency-based visnal attention for rapid scene analysis, IEEE Transactions on Paltem Analysis and Machine Intelligence (PAMI), 1998, Vol. 20(11), pp. 1254-1259.
- [2] Milanese R., Detecting salient regions in an image: from biological evidence to Computer implementation, Ph.D. Thesis, Dept. of Computer Science, University of Geneva, Switzerland, Dec. 1993.
- [3] Culhane S.M., Tsotsos .J.K., A prototype for data-driven visual attention, Proc. ICPR92, Vol. 1, pp. 36-40, Hague, Netherland, 1992.
- [4] Treisman A.M., Gelade G., A feature- integration theory of attention, Cognitive Psychology, 1980, 12, pp. 97-136.
- [5] Aparna Lakshmi Ratan, The role of fixation and visnal attention in object recognition, MIT AI-Technical Report 1529, 1995.
- [6] Todt E., Torras C., Detection of natn- ral landmarks through multi-scale opponent, features, Proc. ICPR. 2000, IEEE Computer Society Press, Vol. 3, pp. 988-1001, Barcelona , Spain, Sep. 2000.
- [7] Onerhani N., Hugli H., Computing visnal attention from scene depth, Proc. ICPR 2000, IEEE Computer Society Press, Vol. 1, pp. 375-378, Barcelona, Spain, Sep. 2000.
- [8] Onerhani N., Bracamonte J., Hugli H., Ansorge M., Pellandini F., Adaptive color image compression based on visual attention, Proc. ICIAP2001, IEEE Computer Society Press, pp. 416-421, Palermo, Italy, Sep. 2001.
- [9] Adams R., Bischof L., Seeded region growing, IEEE Trans, on Pattem, Analysis and Maschine Intelligence, 1994, vol 16, no 6.
- [10] Koethe U., Primary image segmentation. 17 DAGM-Symposiurn, Springer, 1995.
- [11] 0’Gorrnan L., Sanderson A.C., The converging sąuares algorithm: An efficient method for locating peaks in multidimensions, IEEE Trans. Paltem Analysis and Machinę Intelliyence, 1994, PAM1. Vol.6, pp. 280-288.
- [12] Engel S., Zhang X., Wandell B., Colour timing in human visual cortex measured multifunctional magnetic resonance imaging, Nature, 1997, Yol. 388, no. 6637, pp. 68-71.
- [13] Koch Ch., Ullman S., Shifts in selective visual attention: Towards the underlying neural circuity, In L.M. Vaina (edt),Matters of Intelligence, pp. 115-141, 1987.
- [14] Kass M., Witkin A., Terzopoulos D., Snakes: Active contour models, International Journal of Computer Vision, 1988, pp. 321-331.
- [15] A mini A. A., et al, Using dynamie programming for minimizing the energy of active contours in the presence of hard constraints, Proc. Second International Conference on Computer Vision, pp. 95-99, 1988.
- [16] Williams D.J., Mubarak Shah, A fast algorithm for active contours and curvature estimation, CVGIP: Image Understanding, 1992, 55(1), pp. 14-26.
- [17] Ouerhani N., Archip N., Hgli H., Erard P.J., Visual attention guided seed selection for color image segmentation, In: Skarbek, W. (ed.) Computer Analysis of Imag es and Pat- terns. Lecture Notes in Computer Science (LNCS) 2124, Springer Verlag, Berlin 2001, pp. 209-216.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT2-0001-0251
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