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系統識別號 U0026-2708201510224600
論文名稱(中文) 基於基因遺傳演算法的色彩配色之研究
論文名稱(英文) A Study of Color Combination Based on Genetic Algorithm
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 103
學期 2
出版年 104
研究生(中文) 陳威君
研究生(英文) Wei-Chun Chen
電子信箱 weichun.chen836@gmail.com
學號 p36024124
學位類別 碩士
語文別 英文
論文頁數 92頁
口試委員 指導教授-蕭世文
口試委員-林振陽
口試委員-林銘泉
口試委員-王中行
中文關鍵字 基因遺傳演算法  模糊C 平均值  色彩偏好  配色  色彩意象 
英文關鍵字 genetic algorithms  fuzzy c-means  color preference  color combination  color image 
學科別分類
中文摘要 人類的生活環境裡,色彩讓我們盡情地享受豐富、美輪美奐的多彩世界。不論是現代高科技的3C 電子產品如手機,或是到印象派畫家梵谷的畫作星夜,色彩都占有極大的視覺感受與心理意象知覺。以產品研發來說,現今消費市場競爭激烈,色彩成為吸引消費者的重要因素。因此,本研究發展一套以基因遺傳演算法為導向的色彩學習自動化機制,可將學習後的色彩套用至產品、汽車、3D 布料花色、布料花色、品牌配色等研究中,本研究可應用色彩學習的成果於不同設計的層面中,最後再對受測者進行感性的色彩偏好程度問卷評價,探討消費者的心理認知,色彩的學習成果是否與原先學習對象的色彩有相同的心理感覺。
色彩學習機制的對象為本研究所訂定的三大類型圖片:大自然、藝術畫作、族群特色,接續進行食、衣、住、行等色彩的套用,最後運用色彩意象和心理認知,請受測者對配色後的成果進行評價,可同時兼顧客觀:基因遺傳演算法與主觀:受測者對色彩的評價,在兩者間取得最佳的解,透過問卷的施測驗,經過受測者的回饋本研究之色彩配色準確率達海綿寶寶GA1 以87.5%居冠,風景圖GA15 配色準確率85.7%位居第二,梵谷自畫像準確率84.9%排名第三。
最後,將配色準確率與本系統之配色結果的適應函數相驗證,適應函數驗證之結果表明,海綿寶寶GA1 配色平均值0.12,風景圖平均值0.15,梵谷自畫像平均值0.18,一同驗證系統所得之適應函數值,符合問卷施測所得之配色準確率,證實海綿寶寶GA1 之配色結果在問卷與系統中皆排序第一,風景圖GA1 皆排名第二,梵谷自畫像GA1 皆排名第三。
英文摘要 In human's surroundings, it is the color that we can do our heart's content to enjoy colorful and beautiful world. Coloration definately has an important place about visual sense and color preference, whether high-tech consumer electronics like cellphones or impressionism artist Van Gogh's Starry Night. Take product development for example, under the fierce competition today in consumer market, color is the important key to draw consumer's attention.
As a result of that, this research develops a series of color learning automation mechanism based on genetic algorithm. This can take new color in to product, cars, 3D fabric color, fabric color and color combination of brand, etc. This research can apply on different design level by using the color learning achievement. For searching object of study about color learning mechanism, the institute set of three types of pictures: nature, artist works and the characteristic of ethnic group.
Then this study continue to carry on the food, clothing, housing and transportation for the color to use.
Finally, this study can ask for testee to give immediate feeback of the gain of color combination. In that way, the study can simultaneously attend objective:Genetic Algorithm and subjective: the testee's feeback of color, and then the study acquire the optimal solution between both of them. According to the results of questionnaire, the optimal color combination is SpongeBob, and its accuracy is 87.5%. The second place is the image of landscape, and its accuracy is 85.7%. The third place is Van Gogh self portrait, and its accuracy still has 84.9%.
Bringing into the system and getting the results of fitness function then compare with both them of food clothing housing transportation. The average value of fitness function of the color combination of SpongeBob is 0.12, and which is lower than the color combination of scenery picture (0.15) and Van Gogh self portrai (0.18). According to these results, the system of the study can satisfy with cognitive psychology of human being and their needs.
論文目次 摘要 i
SUMMARY ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER 1 INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Purpose of Research 2
1.3 Range and Limitation of Research 2
1.4 Frame of Research 3
CHAPTER 2 PREVIOUS LITERATURES 6
2.1 The Relationship between Color and Emotion 6
2.2 Color Systems Research 6
2.2.1 Musell Color System 7
2.2.2 Oswald Color System 8
2.2.3 PCCS Color System 9
2.2.4 C I E Color System 11
2.2.5 HSV Color Space 12
2.3 Color Psychology 13
2.4 Color Harmony Theory 17
2.5 Aesthetic of Color 17
2.6 The Evolution of the Genetic Algorithm 18
2.6.1 The Evolution of the Genetic Algorithm 19
2.6.2 Application of Genetic Algorithms 21
2.7 Letting and Color Application Association Studies 23
2.7.1 Food and Color Related Research 23
2.7.2 Clothing and Color Related Research 24
2.7.3 Living with the Results of Color Related Research 25
2.7.4 Transportation and Color Studies 26
CHAPTER 3 METHODOLOGY 28
3.1 Clustering and Cluster Analysis 28
3.1.1 Fuzzy C-Means Clustering Method 29
3.2 Genetic Algorithms 30
3.2.1 Genetic Algorithms Defined 30
3.2.2 Gene Algorithm Architecture 36
3.3 RGB and HSV's Conversion Formula 40
3.4 Watershed Algorithm 40
CHAPTER 4 RESEARCH PROCESS 43
4.1 Frame of Research Process 43
4.1.1 Color Separation 44
4.1.2 Learning 48
4.1.3 Utility 51
4.1.4 Estimate 52
4.2 System Establish and Operation 52
4.2.1 History of FCM system 53
4.2.2 Description of System Interface 54
CHAPTER 5 EXPERIMENT OF PROGRAM AND CASE STUDIES 56
5.1 CASE STUDY 56
5.1.1 Limitations of the Case study 56
5.1.2 Case Study: Food, Western Tableware 58
5.1.3 Case Study: Clothing, Printed Skirt 60
5.1.4 Case study: Housing, Living Room 60
5.1.5 Case Study: Transportation, Motorcycle 61
CHAPTER 6 RESULT AND DISCUSSION 63
6.1 Design of Questionnaire 63
6.2 Analysis of Questionnaire 64
CHAPTER 7 CONCLUSION AND SUGGESTION 67
7.1 Conclusion of Study 67
7.2 Suggestion of Study 68
REFERENCES 69
APPENDIXE 77
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