Texture analysis using directional local extrema

• Autorzy: Mavromatis S. , Boï J. M. , Bulot R. , Sequeira J.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a new formalism that enables to take into account textural features of the image in a very robust and selective way. This approach also permits visualization of these features so experts can efficiently supervise an image segmentation process based on texture analysis. The texture concept has been studied through different approaches. One of them is based on the notion of ordered local extrema and is very promising. Unfortunately, this approach does not take into account texture directionality; and the mathematical morphology formalism, on which it is based, does not enable extensions to this feature. This has led us to design a new formalism for texture representation capable of including directionality features. It produces a representation of texture-relevant features in the form of a surface z = f(x, y). The visualization of this surface gives experts sufficient information for discriminating different textures. We illustrate this approach by a set of results showing its interest in the frame of supervised image segmentation.

Słowa kluczowe

EN

image segmentation; texture analysis; scientific visualization; ordered local extrema; directional texture

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2004
• Tom: Vol. 13, No. 3
• Strony: 289--302
• Opis fizyczny: Bibliogr. 14 poz., il.

Twórcy

autor

Mavromatis S.

• Laboratoire des Sciences de l'Information et des Systemes, Équipe LXAO

autor

Boï J. M.

• Laboratoire des Sciences de l'Information et des Systemes, Équipe LXAO

autor

Bulot R.

• Laboratoire des Sciences de l'Information et des Systemes, Équipe LXAO

autor

Sequeira J.

• Laboratoire des Sciences de l'Information et des Systemes, Équipe LXAO

Bibliografia

• [1] Julesz B. Visual pattern discrimination. IT, 8(2), 84-92, February, 1962.
• [2] Horowitz S. L., Pavlidis T.: Picture segmentation by a tree traversal algorithm. ACM, 23(2), 368-388, 1976.
• [3] Gagalowicz A.: Visual discrimination of stochastic texture fields based upon their second order statistics. In ICPR80, 786-788, 1980.
• [4] Canny J. F.: Finding edges and lines in images. In MIT AI TR, 1983.
• [5] Kass M., Witkin A.P., Terzopoulos D.: Snakes: Active contour models. In ICCV87, 259-268, 1987.
• [6] F. Cucu and L. Moltedo. Texture mapping for scientific visualization environments. CG, 17(2), 131-136, 1993.
• [7] Hesselink L., Demarcelle T.: Visualization of vector and tensor data sets. SV, 419-433, 1994.
• [8] Max N., Becker B.: Flow visualization using moving textures. ICASW, 1995.
• [9] Bonnevay S.: Texture feature extraction with the help of regional extremality coding. RECPAD, 1998.
• [10] Khouas L., Odet C. H., Friboulet D.: 3D furlike texture generation by 2D autoregressive synthesis. In WSCG, 1998.
• [11] Scheuermann G., Krüger H., Menzel M., Rockwood A. P.: Visualizing nonlinear vector field topology. TVCG, 4(2), 109-116, 1998.
• [12] Tuceryan M., Jain A. K.: Texture analysis. In HPRCV, 207-248, 1998.
• [13] Mavromatis S., Boi J. M., Sequeira J.: Medical image segmentation using texture directional features. In IEEE EMBC, 2001.
• [14] Mavromatis S., Boi J. M., Sequeira J.: Tissue differentiation by using texture analysis. In BIOENG FARO, 2001.