本研究藉由影像合成法發展窗簾選擇與空間配置模式，輔助消費者、設計師以及傢飾產業專業人員決定理想之窗簾造形與空間配置。首先，針對影像合成法輔助窗簾選擇之擬真效果進行評估，並且深入地探討影響影像合成擬真效果之因子。其次，評估不同色彩之窗簾傢飾布在影像合成的擬真效果，並比較家飾產業專業人員與一般消費者對於擬真效果評估的差異。再者，藉由影像合成法搭配田口方法所發展之決策輔助模式協助探討各種美感之最適化窗簾造型。最後，本研究透過模糊層級分析搭配影像合成法所構建之決策模式輔助決定最適化之空間配置。
研究結果發現灰階對比、材質圖樣與材質透明度均為影響影像合成擬真效果之顯著因子。三個因子的最佳水準組合為60%的灰階影像對比、小材質圖案及60%的材質阻光度，其整體擬真效果達到0.95的程度，顯示影像合成法能有效地應用在傢飾產業。關於不同窗簾布的色彩對於整體擬真效果的評估，淡色材質(金黃色、橘色及淺綠色)的擬真效果明顯優於深色材質(藍黑色、磚紅色及墨綠色)。另外，家飾產業專業人員與一般消費者對整體擬真效果的評價並沒有差異，顯示一般消費者在選擇家飾布及窗簾造型時, 應可參考家飾產業專業人員之建議, 並可即時透過電腦螢幕觀看影像合成結果做為驗證。
窗簾本體、上蓋帷幔、窗紗以及耳幔等四種造型因子影響窗簾之美感，藉由信號/雜訊比與相對應之回應圖，可以找出各種美感之最適化窗簾造型。此外，透過聯合分析決定各造型因子對於各窗簾美感之相對重要性，對於優雅感、實用感以及調和感而言，窗簾本體是最為顯著之造型因子，窗紗是現代感之顯著造型因子，耳幔則為浪漫感與豪華感之顯著造型因子。
此外，本研究藉由模糊層級分析法，可將理性與感性所組合的多準則決策過程，經由設定評估標準，並建立模糊判斷矩陣與權重向量，最終由模糊排序向量中的模糊數決定候選方案的優先排列順序，提供決策者藉由模式評選最適化之室內空間配置。同時，藉由影像合成的應用亦能允許決策者能事先看到空間配置完成的虛擬影像，有助於即時對配置的空間意象進行感性判斷。
本論文所建議之方法能擴及其他傢飾相關產業，提升窗簾選擇及空間配置過程的效率、減少業者與消費者的溝通時間、促進交易，同時提升消費者、設計師與業者間的滿意程度。

This study develops curtain selection and spatial arrangement models based on the image compositing approach to assist consumers, designers and furnishing professionals in optimizing curtain form and spatial arrangement. First, this study evaluates the realistic effect of image compositing in assisting decision makers in curtain selection, and explores the influences on the realistic effect of curtain image compositing. Second, this study examines the effects of different texture color on realistic effect of curtain image compositing and compares furnishing professionals and general consumers in evaluating the realistic effect. Third, this study develops a decision supporting model by integrating the image compositing approach with the Taguchi method to assist decision makers in optimizing the curtain form for various aesthetic feelings. Finally, this study constructs a decision making model by combining the fuzzy analytic hierarchy process (FAHP) approach with the image compositing technique to help decision makers optimize the spatial arrangement.
The analytical results show that grayscale contrast, texture pattern and texture opacity are significant factors influencing the realistic effect of image compositing. Furthermore, the optimum overall realistic effect reaches 0.95 with the combination of 60% grayscale contrast with small texture pattern and 60% texture opacity, indicating that the image compositing approach can effectively be applied in the furnishing related industries. Regarding the evaluation of different texture color to overall realistic effect, light colored textures (golden yellow, orange and light green) were significantly greater than dark colored textures (dark blue, brick red, dark green). Additionally, no significant disparity in assessments existed between furnishing industry professionals and consumers. This phenomenon indicates that consumers should consider the suggestions of furnishing professionals, and obtain immediate verification through virtual images on computer monitors.
The four form factors, curtain body, valance, sheer and cascade, influence curtain aesthetics. Based on the S/N ratio with respect to the response graphs, the optimum curtain form can be determined for various aesthetic feelings. Additionally, the relative importance of the form factors affecting curtain aesthetic feelings can be obtained via conjoint analysis. The curtain body is a significant form factor for elegant, practical and harmonious feelings, sheer is a significant form factor for modern feelings, and cascade is a significant form factor for romantic and luxurious feelings.
Furthermore, this study applies the fuzzy analytic hierarchy process (FAHP) approach to a multiple criteria decision-making process comprising rational and emotional choices. The process involves setting evaluation criteria, constructing a fuzzy judgment matrix and weight vector, and then ranking candidate alternatives by a fuzzy number in the fuzzy sequencing vector. This FAHP approach can allow decision makers to determine the optimum spatial arrangement. Also, creation of an artificial spatial arrangement image by the image compositing technique enables decision makers to see the future completed work before starting renovations, and enables emotional judgment of the spatial image.
The approaches proposed in this study can be extended to furnishing related industries to improve curtain selection and spatial arrangement efficiency, minimize communication time with consumers, promote sales, and improve the degree of satisfaction for consumers, designers and manufacturers.

References
1. Banerjii, R., Bhattacharyya, B.C., 1993. Evolutionary operation (EVOP) to optimize three-dimensional biological experiments. Biotechnology and Bioengineering 41, 67-71.
2. Barbara, R., 1998. Crowd control. Comput. Graph. World 21, 30-32.
3. Bayliss, G.M., Bowyer, A., Taylor, R.I., Willis, P.J., 1994. Virtual manufacturing. Proceedings, CSG 94: Set-Theoretic Solid Modeling Techniques and Applications, Winchester, UK, pp. 353-365.
4. Bendell, J.D., Disney, J., Pridmore, W.A., 1989. Taguchi Methods: Applications in World Industry. IFS Publications, Bedford.
5. Bennett, C., 1977. Spaces for people: Human factors in design. Englewood Cliffs, NJ: Prentice-Hall.
6. Box, G.E.P., Draper, N.R., 1987. Empirical model-building and response surfaces. New York: Wiley.
7. Brinkmann, R., 1999. The Art and Science of Digital Compositing. Morgan Kaufman, Los Altos, CA, Academic Press, San Diego, CA.
8. Chameau J.L., Santamarina J.C., 1987. Membership functions I: Comparing methods of measurement, Int. J. Approximate Reasoning 1, 287-301.
9. Chen, C.Y., Ling, Y.C., 1992. Optimizing electron-capture detector performance for gas chromatographic analysis of polychlorinated biphenyls. Chromatography 33, 272-278.
10. Chen, K.C., Lee, T.C., Houng, J.Y., 1992. Search method for the optimal medium for the production of lactase by Kluyveromyces fragilis. Enzyme and Microbial Technology 14, 659-664.
11. Cheng, C.H., Mon, D.L., 1994. Evaluating weapon system by AHP based on fuzzy scale, Fuzzy Sets and Systems 63, 1-10.
12. Cochrane, J.L., Zeleny, M. (Eds.), 1973. Multiple Criteria Decision Making. The University of South Carolina Press, Columbia.
13. Connacher, H.I., Jayaram, S., Lyons, K., 1995. Virtual assembly design environment. Proceedings of 1995 Computers in Engineering Conference, Boston, MA.
14. Dawson, E.A., Barnes, P.A., 1992. A new approach to the statistical optimisation of catalyst preparation. Applied Catalysis A-General 90, 217-231.
15. Debevec, P.E., Taylor, C.J., Malik, J., 1996. Modeling and rendering architecture from photographs: a hybrid geometry and image-based approach, in: Proc. SIGGRAPH ’96, 11-20.
16. Draper, N.R., Smith, H., 1981. Applied Regression Analysis. Wiley, New York.
17. Ellis, S.R., 1994. What are Virtual Environments? IEEE Computer Graphics and Applications, 14 (1).
18. Evans, G.W., Karwowski, W., Wilhelm, M.R., 1989. Applications of Fuzzy Set Methodologies in Industrial Engineering, Elsevier, Amsterdam.
19. Faugeras, O., Robert, L., 1996. What can two images tell us about a third one? International Journal of Computer Vision 18, 5-20.
20. Finnie, G.R., Witting, G.E., Petkov, D.I., 1993. Prioritizing software development productivity factors using the analytic hierarchy process. Journal of Systems and Software 21, 129-139.
21. Greenbaum, T.L., 1998. The Handbook for Focus Group Research, 2nd Edition. Sage, London.
22. Grobelny, J., 1987. The fuzzy approach to facilities layout problems, Fuzzy Sets and Systems 23, 175-190.
23. Hauser, D., P. Tadikamalla, 1996. The analytic hierarchy process in an uncertain environment: a simulation approach, European Journal of Operational Research 91(1), 27-37.
24. Hepler, D.E., Wallach, P.I., 1987. Architecture: Drafting and Design, 5th ed., Glencoe Macmillan/McGraw-Hill, New York.
25. Houng, J.Y., Chen, K.C., Hsu, W.H., 1989. Optimization of cultivation medium composition for isoamylase production. Applied Microbiology and Biotechnology 31, 61-64.
26. Jang, H., Lee, J.S., Fash, J.W., 2001. Compositional effects of the brake friction material on creep groan phenomena, Wear 251, 1477– 1483.
27. Jayaram, S., Connacher, H.I., Lyons, K.W., 1997. Virtual assembly using virtual reality techniques. Computer-Aided Design 29, 575-584.
28. Kim, S.J., Jang, H., 2000. Friction and wear of friction materials containing two different phenolic resins reinforced with aramid pulp, Tribology International 33, 477–484.
29. Krueger, R.A., 1988. Focus Groups: A Practical Guide for Applied Research. Sage, London.
30. Krueger, R.A., King, J.A., 1998. Involving Community Members in Focus Groups 5, The Focus Group Kit. Sage, London.
31. Laarhoven, P.J.M., Pedrycz, W., 1983. A Fuzzy Extension of Saaty's priority theory, Fuzzy Sets and Systems 11 (3), 229-241.
32. Laubli, Th., Hunting, W., Grandjean, E., 1982. Visual impairments in VDU operators related to environmental conditions. In E. Grandjean and E. Vigliani (eds), Ergonomic aspects of visual display terminals. London: Taylor & Francies, 85-94.
33. Lee, C.C., 1990. Fuzzy logic in control systems: Fuzzy logic control-PartsⅠand Ⅱ, IEEE Transactions on Systems, Man and Cybernetics 20, 404-435.
34. Lee, E.S., Li, R.L., 1988. Comparison of fuzzy numbers based on the probability measure of fuzzy events, Computational Mathematics and Applications 15, 887-896.
35. Lee, W.B., Lau, H., Liu, Z.Z., Tam, S., 2001. A fuzzy analytic hierarchy process approach in modular product design. Expert System 18 (1), 391-419.
36. Levary, R.R., Ke, W., 1998. A simulation approach for handling uncertainty in the analytic hierarchy process. European Journal of Operational Research 106 (1), 116-122.
37. Lindman, H.R., 1974. Analysis of variance in complex experimental designs. San Francisco: W.H. Freeman & Co.
38. Machover, C., Tice, S.E., 1994. Virtual reality. IEEE Computer Graphics and Applications, 14 (1).
39. Millet, I., Harker, P.T., 1990. Globally Effective Questioning In the Analytic Hierarchy Process, European Journal of Operational Research 48, 88-97.
40. Mitta, D.A., 1993. An application of the Analytic Hierarchy Process: a Rank-Ordering of Computer Interfaces. Human Factors 35 (1), 141-157.
41. Montgomery, D.C., 1997. Design and Analysis of Experiments, Wiley, New York.
42. Peace, G.S., Taguchi Methods: A Hands-on Approach, Addison-Wesley, Reading, MA, 1993.
43. Mundy, J.M., Zisserman, A. (Eds.), 1992. Appendix-projective geometry for machine vision. In: Geometric Invariance in Computer Vision. MIT Press, Cambridge.
44. Nakai, S., Koide, K., Eugster, K., 1984. A new mapping supersimplex optimization for food product and process development. Journal of Food Science 49, 1143-1170.
45. Newbold, P., 1988. Statistics for Business and Economics. Prentice-Hall International Editions, Englewood Cliffs, NJ.
46. Nojiri, H., 1982. A model of executive’s decision process in new product development, Fuzzy Sets and Systems 7, 227-241.
47. Norusis, M.J., 1990. SPSS Base System User’s Guide. SPSS Inc., Chicago.
48. Ross, T.J., 1995. Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
49. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15, 234-281.
50. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York.
51. Scheffé, H., 1953. A method for judging all contrasts in the analysis of variance, Biometrika 40, 87-104.
52. Stammerjohn, L., Smith, M., Cohen, B., 1981. Evaluation of workstation design factors in VDT operations. Human Factors 23, 401-412.
53. Stone, R.A., Veevers, A., 1994. The Taguchi influence on designed experiments. Journal of Chemometrics 8, 103-110.
54. Tabucanon, M. T., Batanov, D.N., Verma, D.K., 1994. Decision support system for multi-criteria machine selection for flexible manufacturing systems, Computers in Industry 25 (2), 131-143.
55. Taguchi, G., 1987. System of Experimental Design. Engineering Methods to Optimize Quality and Minimize Costs. Kraus International, White Plains, NY.
56. Taguchi, G., Phadke, M.S., 1984. GLOBECOM 84 Meeting, IEEE Communication Society, Atlanta, GA, November.
57. Tunga, R., Banerjii, R., Bhattacharyya, B.C., 1999. Optimization of n variable biological experiments by evolutionary operation-fractional design technique. Journal of Bioscience and Bioengeering 87 (2), 224-230.
58. Wang, M.J., Lee, Y.J., 1997. Applying the AHP approach to evaluate human sensitivity to chromatic light, Behaviour & Information Technology 16 (6), 348-358.
59. Watt, A., 1993. 3D Computer Graphics, 2nd ed., Addison-Wesley, Reading, MA, 54-98.
60. Wolberg, G., 1990. Digital Image warping, IEEE Computer Society Press, Los Alamitos, CA.
61. Wu, F.G., Lee, Y.J., Chen, C.H., 2003. Evaluation of the realistic effect of image compositing to assist in curtain selecting. International Journal of Industrial Ergonomics 32 (1), 1-12.
62. Yang, W.H., Tarng, Y.S., 1998. Design optimization of cutting parameters for turning operations based on the Taguchi method, Journal of Materials Processing Technology 84, 122–129.
63. Yu, P.L., 1985. Multiple Criteria Decision Making: Concepts, Techniques, and Extensions. Plenum Press, New York.
64. Zadeh, L.A., 1965. Fuzzy sets, Information and Control 8, 338-353.
65. Zeleny, M., 1982. Multiple criteria decision making, McGraw-Hill, New York.
66. Zhang, Z.Y., 1995. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence Journal 78, 87-119.
67. Zimmermann, H.J., 1987. Fuzzy Sets, Decision Making, and Expert Systems, Kluwer Academic Publishers, Boston, MA.
68. Zimmermann, H.J., 1993. Fuzzy Sets and its Applications, 2nd edn, Kluwer Academic Publishers, Boston, MA.
69. Zwicky, F., 1967. The morphological approach to discovery, invention, research and construction, In: F. Zwicky & A. G. Wilson (eds.), New methods of Thought and Procedure; Symposion on Methodologies, Pasadena, May. Berlin: Springer, 316-317.