The fractal estimation of wood color variation by the triangular prism surface area method

被引：15

作者：

Liu J. ^{[1
]}

Furuno T. ^{[1
]}

机构：

[1] Shimane University, Faculty of Science and Engineering, Matsue, Shimane 690-8504

来源：

Wood Science and Technology | 2002年 / 36卷 / 05期

关键词：

Algorithms; -; Color; Fractals; Hardwoods; Prisms; Softwoods;

D O I：

10.1007/s00226-002-0148-2

中图分类号：

学科分类号：

摘要：

Color variations of the surfaces of fifteen wood species were characterized by fractal dimension of the triangular prism surface area method. Softwood and hardwood indicated apparently different in the mean fractal dimensions of red and green colors. Red color behaved steadier in softwood than in hardwood and green color varied comparatively stronger in hardwood than in softwood. No evident differences between softwood and hardwood were found in the variation of blue color of all the specimens. Following the low-to-high value order of the mean fractal dimensions, three types of combination of red (R), green (G) and blue (B) colors were found: RGB, RBG and BGR. There also existed six types of fractal dimension distribution; namely, plane, included plane, concave, convex, zigzag, and hilly distributions. Fractal dimensions across the grain changed greatly whereas those along the grain became relatively steady. The characteristic difference of color variation was defined for each species, which was inferred to characterize its own unique appearance of surface color. For color matching of wood parts, fractal dimension quantitatively furnishes essential information of color variation in local and overall features. Such evaluation can be efficiently carried out with few measurements along the grain and by detecting a single color (red, green or blue) only.

引用

页码：385 / 397

页数：12

共 19 条

[1] Beckwith J.R., Theory and practice of hardwood color measurement, Wood Sci., 11, 3, pp. 169-175, (1979)
[2] Brunner C.C., Shaw G.B., Bulter D.A., Funck J.W., Using color in machine vision systems for wood processing, Wood Fiber Sci., 22, 4, pp. 413-428, (1990)
[3] Butler D.A., Brunner C.C., Funck J.W., A dual-threshold image sweep-and-mark algorithm for defect detection in veneer, Forest Prod. J., 39, 5, pp. 25-28, (1989)
[4] Clark K.C., Computation of the fractal dimension of topographic surfaces using the triangular prism surface area method, Computers & geosciences, 12, 5, pp. 713-722, (1986)
[5] Forrer J.B., Butler D.A., Funck J.W., Brunner C.C., Image sweep-and-mark algorithms: Part 1. Basic algorithms, Forest Prod. J., 38, 11-12, pp. 75-79, (1988)
[6] Forrer J.B., Butler D.A., Funck J.W., Brunner C.C., Image sweep-and-mark algorithms: Part 2. Performance evaluations, Forest Prod. J., 39, 1, pp. 39-42, (1989)
[7] Lebow P.K., Brunner C.C., Maristany A.G., Butler D.A., Classification of wood surface features by special reflectance, Wood Fiber Sci., 28, 1, pp. 74-91, (1996)
[8] Liu J., Estimation of wood textures using fractal dimension, (1999)
[9] Maristany A.G., Lebow P.K., Brunner C.C., Application of the dichromatic reflection model to wood, Wood Fiber Sci., 26, 2, pp. 249-258, (1994)
[10] Masuda M., Visual characteristics of wood and the psychological images, Mokuzai Gakkaishi (Journal of the Japan Wood Research Society), 38, 12, pp. 1075-1081, (1992)

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