在平面圖形設計或產品造形設計過程中，一個有效的圖形美學評價工具，能夠輔助設計師進行的設計決策，現有最常使用的熵值評價方法，因為分析的訊息項目不足，可供辨識的基準較少，無法對圖形進行有效的美學評價與辨識工作，因此，發掘更多的圖形美學分析項目就可以提供更多的圖形美學辨識基準，本研究改善現有的熵值方法並提出一個新的圖形風格特徵評價方法，可應用於2D圖形的特徵評價，作為量化的圖形設計決策基準；首先，為了簡化計算量，由線型構成分析與特徵點轉換規則將圖形轉換成圖形特徵點(SFP)的集合；然後，分析圖形特徵點集合的元素與比例，以及元素所產生的各項效果，可獲得8項圖形特徵評價，包含四項圖形組成特徵評價：線型複雜度H(t)、特徵點複雜度H(p)、位置複雜度H(r)、圖形構成複雜度H(s)；與四項圖形力學特徵評價：平衡度I(b)、運動度I(m)、平滑度I(s)與張力度I(t)等，利用各特徵值的強弱表現，可用以描述圖形的特點，藉此，圖形間的差異也可輕易的辨識出來；最後，以平面商標案例進行8項圖形特徵值評價，結果顯示，本方法確實可有效的進行圖形特徵評價，各特徵值的強弱度，提供了設計人員解讀圖形差異的依據，可用於圖形近似度的判定，或進行圖形設計時的決策依據。

In the graphic design or the product shape of the design process, an effective graphical aesthetic evaluation tools can assist the designers to make decision in design process. The existing entropy of evaluation method which most commonly used are still not incapable to recognize and assessing the shape efficiently because of lack of project analysis and less reference for identification. The graphics cannot be effective for aesthetic evaluation and identification, therefore, to explore more graphic aesthetic analysis project can provide more graphics and aesthetics reference. This study is to improve the existing method of entropy and to propose a new graphical style characteristics evaluation method that can be applied to evaluate the characteristics of 2D graphics features as a reference to quantitative benchmarks pf graphic design decisions; The purpose is to simplify the calculation by the linear structure analysis point conversion rule will be converted into a collection of graphic pattern feature point shape feature points (SFP); and then, the effect of the elements and proportions, as well as elemental analysis pattern feature set of points generated. Eight graphics are taken to evaluate the aesthetic characteristics and composition of the evaluation contains four patterns: linear complexity H(t), the complexity of the feature point H(p), the location of the complexity H(r), the graphic complexity H(s)；and mechanical features for four graphic assessment: balance I(b), the degree of motion I(m), the smoothness of the I(s) and the elasticity intensity of the graphic I(t) et cetera. By using the strength of the performance of each characteristic values can be used to describe the aesthetic expression pattern. Whereby the difference between the graphics can be easily identified and isolated. Finally the eight cases of the trademark graphic feature value of the evaluation, from the result shows that this method can effectively evaluate graphical features, the strength of each characteristic values can help the graphic designers to interpret the differences of the graphics based on the degree of approximation or make decision based on the graphic design work.

Attneave, F. (1957). Physical determinants of the judged complexity of shapes. Journal of Experimental Psychology, 53(4), 221.
Bai, X., Yang, X., Yu, D., & Latecki, L. J. (2008). Skeleton-based shape classification using path similarity. International Journal of Pattern Recognition and Artificial Intelligence, 22(04), 733-746.
Bloch, P. H. (1995). Seeking the ideal form: product design and consumer response. The Journal of Marketing, 16-29.
C.-T. Young, S.-T. W. (2005). The Association of Complexity of Figures and Dimensional Visual Illusion-A Study of Irregular Polygons. Journal of National Taiwan College of Arts, 77, 13-23.
Chandrasegaran, S. K., Ramani, K., Sriram, R. D., Horváth, I., Bernard, A., Harik, R. F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design, 45(2), 204-228.
Chen, L.-C., & Chu, P.-Y. (2012). Developing the index for product design communication and evaluation from emotional perspectives. Expert Systems with Applications, 39(2), 2011-2020.
Chen, L. C., Hsieh, J. W., Yan, Y., & Chen, D. Y. (2015). Vehicle make and model recognition using sparse representation and symmetrical SURFs. Pattern Recognition, 48(6), 1979-1998.
Cross, N. (2008). Engineering design methods: strategies for product design. John Wiley & Sons.
Deng, J.-L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294.
Deng, J.-L. (1988). Properties of relational space for grey system. Essential topics on grey system theory and applications, 1-13.
Day, H. (1968). The importance of symmetry and complexity in the evaluation of complexity, interest and pleasingness. Psychonomic Science, 10(10), 339-340.
Forsythe, A. (2009). Visual Complexity: Is That All There Is?. Engineering Psychology and Cognitive Ergonomics, 158-166. Springer Berlin Heidelberg.
Fuchs, C., & Schreier, M. (2011). Customer empowerment in new product development. Journal of Product Innovation Management, 28(1), 17-32.
Hsiao, S.-W., Chiu, F.-Y., & Chen, C. S. (2008). Applying aesthetics measurement to product design. International Journal of Industrial Ergonomics, 38(11), 910-920.
Hsiao, S.-W., Hsu, C.-F., & Lee, Y.-T. (2012). An online affordance evaluation model for product design. Design Studies, 33(2), 126-159.
Hsu, S. H., Chuang, M. C., & Chang, C. C. (2000). A semantic differential study of designers’ and users’ product form perception. International Journal of Industrial Ergonomics, 25(4), 375-391.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2), 179-187.
Intellectual Property Office (2016). Trademark search system Website. Retrieved from http://tmsearch.tipo.gov.tw/TIPO_DR/index.jsp
Kishore, P. V. V., Prasad, M. V. D., Prasad, C. R., & Rahul, R. (2015). 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN. In Signal Processing And Communication Engineering Systems (SPACES), 2015 International Conference on, 34-38. IEEE
Komoto, H., & Tomiyama, T. (2012). A framework for computer-aided conceptual design and its application to system architecting of mechatronics products. Computer-Aided Design, 44(10), 931-946.
Liu, M. (2010). Fingerprint classification based on Adaboost learning from singularity features. Pattern Recognition, 43(3), 1062-1070.
López-Fernández, D., Madrid-Cuevas, F. J., Carmona-Poyato, A., Muñoz-Salinas, R., & Medina-Carnicer, R. (2016). A new approach for multi-view gait recognition on unconstrained paths. Journal of Visual Communication and Image Representation, 38, 396-406.
Luo, L., Kannan, P., & Ratchford, B. T. (2008). Incorporating subjective characteristics in product design and evaluations. Journal of Marketing Research, 45(2), 182-194.
Ngo, D. C. L., Teo, L. S., & Byrne, J. G. (2003). Modelling interface aesthetics. Information Sciences, 152, 25-46.
MATSUOKA, Y. (2000). Shape-Generation Method Using Curvature Entropy. KANSEI Engineering International, 1(4), 11-18.
Mizuno, S., & Akao, Y. (1978). Quality function deployment: a company wide quality approach. JUSE Press, Tokyo.
Moon, H., Chellappa, R., & Rosenfeld, A. (2002). Optimal edge-based shape detection. Image Processing, IEEE Transactions on, 11(11), 1209-1227
Orbay, G., Ersin Yümer, M., & Kara, L. B. (2012). Sketch-based aesthetic product form exploration from existing images using piecewise clothoid curves. Journal of Visual Languages & Computing, 23(6), 327-339.
Peng, H. L., & Chen, S. Y. (1997). Trademark shape recognition using closed contours. Pattern Recognition Letters, 18(8), 791-803.
Pu, J., & Ramani, K. (2006). On visual similarity based 2D drawing retrieval. Computer-Aided Design, 38(3), 249-259.
Ramesh, B., Xiang, C., & Lee, T. H. (2015). Shape classification using invariant features and contextual information in the bag-of-words model. Pattern Recognition, 48(3), 894-906.
Ranscombe, C., Kinsella, P., Stoddart, P. R., & Melles, G. (2015). Investigating Shape Comparison Tools for Benchmarking Differences in Product Appearance During Product Styling. ICoRD’15–Research into Design Across Boundaries, 1, 169-179.
Schürmann, J. (1996). Pattern classification: a unified view of statistical and neural approaches. Wiley Online Library.
Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication; Univ. of Illinois. Urbana, 114.
Tang, C., Fung, K., Lee, E. W., Ho, G. T., Siu, K. W., & Mou, W. (2013). Product form design using customer perception evaluation by a combined superellipse fitting and ANN approach. Advanced Engineering Informatics, 27(3), 386-394.
Thurston, C. (1945). The" Principles" of Art. The Journal of Aesthetics and Art Criticism, 4(2), 96-100.
Tsai, H.-C., & Hsiao, S.-W. (2004). Evaluation of alternatives for product customization using fuzzy logic. Information Sciences, 158, 233-262.
Ujiie, Y., & Matsuoka, Y. (2009). Macro-informatics of cognition and its application for design. Advanced Engineering Informatics, 23(2), 184-190.
Wang, H. (1995). An approach to computer-aided styling. Design Studies, 16(1), 50-61.
Wei, C. H., Li, Y., Chau, W. Y., & Li, C. T. (2009). Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recognition, 42(3), 386-394.
Wilder, J., Feldman, J., & Singh, M. (2011). Superordinate shape classification using natural shape statistics. Cognition, 119(3), 325-340.
Young, I., & Ziemer, F. (1985). A Three-Dimensional Analysis of Marine Radar Images for the Determination of Ocean Wave Directionality and Surface Currents. Journal of geophysical research, 90(C1), 1049-1059.
Zhang, B., & Seo, H. S. (2015). Visual attention toward food-item images can vary as a function of background saliency and culture: An eye-tracking study. Food Quality and Preference, 41, 172-179.
Zhang, Y., Yang, J., Wang, S., Dong, Z., & Phillips, P. (2016). Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Journal of Experimental & Theoretical Artificial Intelligence, 1-14.
Žunić, D., & Žunić, J. (2014). Shape ellipticity from Hu moment invariants. Applied Mathematics and Computation, 226, 406-414.