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研究生:林孟緯
研究生(外文):Meng-Wei Lin
論文名稱:基於空間模糊C-means分群演算法之多重光源估測與其在影像白平衡之應用
論文名稱(外文):Multi-illuminant Estimation Based on Spatial Fuzzy C-means Clustering with Application to Image White Balance
指導教授:陳正倫陳正倫引用關係
口試委員:郭彥甫楊朝龍
口試日期:2016-07-26
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:157
中文關鍵詞:多重光源估測空間模糊C平均法白平衡
外文關鍵詞:Multi-illuminant estimationSpatial fuzzy c-meansWhite balance
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在多數場景中,以數位擷取裝置拍攝之影像深受環境光源之影響。由於影像中之物體色彩隨不同光源而產生變動甚至與光源產生同樣色調,導致擷取之影像無法還原物體真實的色彩,此現象稱為色偏。本論文利用基礎的顏色科學和實驗的結果建立不同光源下的色彩樣本,接著以現有之光源估測演算法計算影像複數像素色溫為初估色溫; 再利用分群演算法,將該複數初估色溫進行光源分類,以計算影像之多重決定色溫。本研究率先使用空間模糊C-means分群演算法來進行光源分類,同時也與不同分群演算法進行比較,且在分析實驗結果上,能利用色溫分布圖與直方圖呈現出主要光源的分布情形,並觀察各個分群演算法分類光源的能力。最後依照各個分群法計算出決定色溫與標準人工日光之色溫計算修正比例值,並對影像進行白平衡。由於傳統白平衡為單一光源場景的修正架構,其修正法為固定修正值,然而現實的攝影環境多半為多光源環境,如將整張影像以固定修正值做白平衡修正者,將會使整個影像中某些色調的像素往暖色系或冷色系偏移,使得白平衡的效果並不佳。而本論文採用之SFCM演算法,對主要光源作個別修正後,成功解決了多光源影像不易處理色偏之問題。而除了白平衡在影像色彩的修正外,亦提出可進行簡易分析之色彩辨識與臉部辨識之應用,強化了白平衡技術的實用性。

In most scenes, images captured by digital devices are greatly influenced by environmental illuminant. Because there are different illuminants in the scene, the color from objects even change and be similar to the illuminant color. It results that the objects cannot reflect the true color and this phenomenon is called color cast. In this thesis, by utilizing basic color science and experimental results, a dataset of color sample under different illuminants is constructed. Using the existing method of illuminant estimation to calculate plural color temperatures of the input image, called the initial estimated color temperature. Furthermore, utilizing clustering algorithm to execute illuminant classification to initial plural color temperatures, and calculating the multi-determined color temperatures of image. This study takes the lead in utilizing the spatial fuzzy c-means (SFCM) clustering algorithm to execute the illuminant classification. Moreover, there is a comparison with other clustering algorithms. By using distribution of color temperature and histogram plot, which present the main illuminant distribution and they can be observed the ability of illuminant classification. Finally, taking multi-determined color temperatures from each clustering algorithm and color temperature from standard artificial daylight to calculate the correction ratio to white balance. In traditional white balance, the correction configuration focuses on single illuminant scene, which is fixed correction value, whereas there are multi-illuminant scenes mostly in real scene. If the image is revised by fixed correction value in white balance progress, it will result that a certain group of tone colors tend to be warm color or cool color. Finally, the performance of white balance is not good. In this thesis, the adopted SFCM algorithm revises the main illuminant individually and solves the color cast successfully. In addition to the color correction to white balance, another simple applications are presented for color detection and face recognition by executing white balance. They enhance the applicability of white balance.

Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Literature Review 2
1.2.1 Existing Single Illuminant Estimation Methods 3
1.2.2 Existing Color/White Balance Methods 12
1.2.3 Existing Multi-illuminant Estimation Methods 17
1.3 Organization and Contribution 24
Chapter 2 Fundamentals 26
2.1 Illuminant 26
2.1.1 Color Temperature 26
2.1.2 Illuminant Chromaticity 27
2.2 Color Space 28
2.3 System Description 30
2.3.1 Image Objects and Capturing Device 30
2.3.2 Study on Multi-illuminant Scenario 32
Chapter 3 Single Illuminant Estimation 39
3.1 Methodology 39
3.2 White Balance 41
Chapter 4 Multi-illuminant Estimation 44
4.1 Motivation 44
4.2 Methodology 46
4.2.1 K-means Based on Chromatic Distance 48
4.2.2 Fuzzy C-means Based on Membership Function (FCMMF) 51
4.2.3 Fuzzy C-means Based on Chromatic Distance (FCMCD) 53
4.2.4 Spatial Fuzzy C-means (SFCM) 54
4.2.5 Effects of Preprocessing on Clustering 59
4.3 Experimental Results 61
4.3.1 Synthetic Images 61
4.3.2 Scenes from Real World 101
4.3.3 Comparative Study 130
4.3.4 Issues with Warm and Cool Colors 137
4.4 Determination of Optimized Number of Illuminants 140
Chapter 5 Application 144
5.1 Color Detection 144
5.2 Face Recognition 147
Chapter 6 Conclusion and Future Work 151
References 153


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