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研究生:楊孟樺
研究生(外文):Meng-HuaYang
論文名稱:系統性方法於產品多色配色計劃之應用研究
論文名稱(外文):A systematic method for colour planning in multi-coloured product design
指導教授:蕭世文蕭世文引用關係
指導教授(外文):Shih-Wen Hsiao
學位類別:博士
校院名稱:國立成功大學
系所名稱:工業設計學系
學門:設計學門
學類:產品設計學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:72
中文關鍵詞:色彩調和審美度色彩配色消費型態因素層級分析色彩意象
外文關鍵詞:colour harmonyaesthetic measurecolour matchingconsumption style factoranalytic hierarchy processcolour image
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本研究提出一項以審美度計算為基礎的色彩趨勢預測系統。消費者的色彩意象變化往往受環境因素所影響,因此以層級分析法獲得經濟、教育、文化和科技這4項環境因素的權重關係。接著將4項環境因素分別對映以問卷方式所選取的6個意象做型態評估,並加權評價後做級距分級,以瞭解環境因素與意象的關係。在實驗的的過程用P.C.C.S系統取樣出111個色票,且以審美度方式計算出90個三色配色組合,再與6個意象做模糊評價,並經由K平均演算法集群分析成6個群組。最後以判定平均值和標準差的方式找出每個群組分別對映的意象,瞭解色彩與意象的關係。
根據上述實驗流程獲得消費因素、意象和色彩三者的相互關係,即可建構一個符合市場需求的色彩趨勢預測系統。在該系統的輔助下,可以獲得汽車市場的最佳三色配色效益。本研究以汽車為例,亦可以此研究方法套用於其他產品的色彩計畫。
A colour design/selection system for predicting the color trend based on aesthetic measures is proposed in this article. In this method, the analytic hierarchy process theory was used to evaluate the weights for 4 major consumption style factors in human environment, including Economy, Education, Culture, and Technology. Then 6 product image words were selected by using the questionnaire, and mapped the image words and the factors with the weighted results and clustered both variables with the corresponding ranks. During the implementation procedure, 111 colours samples distributed covering the entire domain of the PCCS (Practical Color Co-ordinate System) colour system were taken, and the aesthetic measure for 3-coloured harmony based on aesthetic measure theory was calculated. Then 90 tri-coloured combinations were divided into six clusters based on the six image words by using k-means clustering. The 3-coloured products were then mapped into each cluster with the calculated grand average and Standard Deviation of the image word values obtained for the 3-coloured products. According to the above implementation logic, the relationship among the consumption style factors, the image words, as well as the tri-coloured cars can be gotten, which were then used to construct a colour trend based on market requirement. With the aid of this system, one can get a 3-coloured car to match his/her requirement. Although the selection of 3-coloured car is taken as an example to specify the methodology, it can also be used to develop a system for other products.
摘要 i
SUMMARY ii
謝誌 iii
LIST OF TABLES vi
LIST OF FIGURES viii
CHAPTER 1 INTRODUCTION 1
1.1 Research motivation 1
1.2 Research objectives 2
1.3 Research framework 2
1.4 Literature review 4
CHAPTER 2 THEORETICAL BACKGROUND 6
2.1 Fuzzy mathematics 6
2.1.1 Representation of fuzzy set 6
2.1.2 Fuzzy relations 7
2.1.3 Fuzzy matrices 7
2.2 K-means clustering 8
2.3 Analytic hierarchy process 9
2.4 Basic procedure for calculating the aesthetic measure for colour harmony 11
2.5 Colour area 15
CHAPTER 3 CONSTRUCTION OF THE AESTHETIC MEASUREMENT FORMULA OF COLOUR MATCHING 17
3.1 Decision of image words 17
3.2 Formation of a colour palette for the experiment 18
3.3 Construction of the aesthetic measurement formula 20
3.4 Implementation procedure 24
3.5 Case studies 25
3.5.1 Case study I 25
3.5.2 Case study II 34
3.6 Verification of the experiments 41
3.6.1 Case study I 41
3.6.2 Case study II 45
CHAPTER 4 EXPERIMENTAL PROCESS FOR PREDICTING THE CAR COLOUR TREND 50
4.1 Selection of the subject and observers 50
4.2 Colour palette formulated for experiment 53
4.3 Decision of the product image 54
4.4 Calculate the rank of optimal tri-coloured samples 54
4.4.1 Subject in a given solid visual angle 54
4.4.2 Divide the subject product into three parts 54
4.4.3 Choose symbolic combination colour for 6 product images 55
4.5 Evaluation cluster of colour emotion of consumers 56
4.6 Constructing the relationship between the consumption style and product images 58
4.6.1 Evaluate of weighting functions 59
4.6.2 Calculating the relationship between the consumption style and product images 60
CHAPTER 5 CASE DEMONSTRATION AND VERIFYING THE PREDICTING SYSTEM 63
5.1 Construct a colour trend predicting system based on market requirement 63
5.2 Verifying the effect of the predicting system 65
CHAPTER 6 CONCLUSION 66
REFERENCES 68
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