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研究生:曾建中
研究生(外文):Chien-ChungTseng
論文名稱:藉由特徵形狀及位置的內外類別相對關係來合成其誇張卡通圖
論文名稱(外文):Exaggerative Caricature Creation Using Inter- and Intra-Correlations of Feature Shapes and Positions
指導教授:連震杰
指導教授(外文):Jenn-Jier Lien
學位類別:博士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:105
中文關鍵詞:誇張卡通圖內外類別相對關係適應性的區域保護投影
外文關鍵詞:Exaggerative CaricatureInter- and Intra-CorrelationsAdaptive Locality Preserving Projection
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本論文發展了一種誇張化或是動物化的彩色人臉卡通圖產生系統,包括一個藉著統計方式來進行形狀特徵的誇張模組 (Statistics-Based Shape Exaggeration (SBSE) module),一個藉著比對人臉動物特徵來進行動物化形狀誇張模組 (Animal-Like Shape Exaggeration (ALSE) module),以及一個非擬真著色模組 (Non-Photorealistic Rendering (NPR) module) 來自動產生具有誇張化或是動物化的彩色卡通圖,並且保有對每個人獨特細節(例如,鬍子、髮型、痣)的描寫。不像以往的研究只著重在考慮外部類別相互關係 (即比較輸入影像的臉部器官與訓練資料庫中的平均臉部器官的差異),SBSE 模組使用的是一種同時考慮臉部器官的對內部以及外部類別相對關係並以遞迴的方法來自動誇張人臉影像的方法。而所謂的內部類別相對關係在本論文中考量的是對某一個臉部器官與在同一個輸入人臉上的其他臉部器官來比較其相對的關係,此內部類別相互關係具有誇張主要臉部特徵及抑制非主要臉部特徵的影響。而在 ALSE 模組中首先採用了適應性的區域保護投影 (Adaptive Locality Preserving Projection (ALPP)) 方法來得到一個保有高維資料分佈間之區域結構及線性關係的低維度子空間,並將資料投影到這空間上來得到降維的效果。接著,利用大型邊緣最近鄰居矩陣學習 (Large Margin Nearest Neighbor (LMNN) metric learning),並根據降維後的資料,利用將同類拉近、不同類分開來建立一個具有鑑別度的空間並且利用k-NN分類器來分類輸入人臉屬於哪種相似的動物類別,最後合成該動物與人臉間混合的動物化特徵形狀。非擬真著色模組包含了一個產生具有特徵邊緣以及暗色區塊的黑白素描的過程,一個產生彩色卡通素描的過程以及一個影像變形過程。當產生彩色卡通素描後,最後會利用藉著 SBSE 模組和 ALSE 模組所產生的誇張特徵形狀與原本輸入的特徵形狀之間的來產生一個轉換關係,並藉由這個轉換關係將彩色卡通素描變形成一張誇張化或動物化的彩色卡通圖。經由實驗結果可以證明本文所提的系統可以強調主要的臉部特徵,並且保有與原圖一定的相似程度。產生的人臉卡通,其生動的效果比以往只考慮內部關係的方法還要出色。此外,透過充分的比較,證明所提出的ALPP方法在充足的訓練資料下表現比以往的降維方法還要來的好。
This study develops an exaggerative facial caricature generation system comprising a Statistics-Based Shape Exaggeration (SBSE) module, an Animal-Like Shape Exaggeration (ALSE) module and a Non-Photorealistic Rendering (NPR) module for the automatic creation of colored facial caricatures with exaggerative or animal-like facial features and individual details such as beards and moles. Unlike previous research that focused on the inter-correlation (the difference between the facial features of input image and those of the mean face in the training database), the SBSE module exaggerates the input image utilizing an iterative approach based on both inter- and intra-correlations of the facial features. The intra-correlation considered in this study makes the comparison with other features within the same input image, and has the effect of exaggerating the major facial features while simultaneously subduing the visual impact of non-major facial features. In the ALSE module, Adaptive Locality Preserving Projection (ALPP) method is proposed first to obtain a low-dimensional spatial subspace in which the linearity and local structure of high-dimensional data distribution are preserved. All the training data is projected on this ALPP subspace, and the Large Margin Nearest Neighbor (LMNN) metric learning method is applied to construct a discriminant subspace for k-NN classifier to classify the animal class of input facial image. Then, the animal-like feature shape is synthesized by combining the feature shape of classified animal and input facial image. The NPR module consists of a black-and-white sketch creation process, a colored cartoon-like sketch creation process, and an image warping process. The colored cartoon-like sketch is then warped into a colored exaggerative or animal-like caricature based on the transformation function of original feature-shape and corresponding exaggerative or animal-like feature-shapes created by the SBSE and ALSE modules, respectively. The experimental results demonstrate that the proposed method can emphasize the major characteristics of a face and preserve the individual details (such as mole, hair style, or beards) simultaneously. The produced caricatures are more vivid than previous methods that only considered inter-correlation. Also, by using the comprehensive comparison, it can be seen that the proposed ALPP outperforms other dimensionality reduction methods under sufficient training data.
中文摘要... IV
Abstract... VI
誌謝... VIII
Table of Contents...IX
Lists of Tables...XII
Lists of Figures...XIII
Ch. 1 Introduction...1
1.1 Motivation...1
1.2 Related Works...5
1.3 Contributions...9
Ch. 2 Exaggerative Caricature Creation System...12
Ch. 3 Statistics-Based Shape Exaggeration (SBSE) Module...16
3.1 Feature-Shape Eigenspaces and Averaged Distance-from-Center Vectors Creations...16
3.2 Exaggerative Feature-Shape Vector Creation: Inter- Exaggeration Process...19
3.3 Exaggerative Feature-Shape Vector Creation: Intra- Exaggeration Process...22
3.4 Exaggerative Distance-from-Center Vector Creation: Inter- Exaggeration Process...26
3.5 Exaggerative Distance-from-Center Vector Creation: Intra- Exaggeration Process...27
3.6 Iterative Computation Between Inter- and Intra-Correlations...32
Ch. 4 Animal-Like Shape Exaggeration (ALSE) Module...35
4.1 Facial and Animal Feature-Shape Mapping Creation...37
4.2 Dimensionality Reduction of Facial Feature-Shape Using Adaptive Locality Preserving Projection (ALPP)...40
4.3 Discriminant Subspace Creation of Facial Feature-Shape Using Large Margin Nearest Neighbor (LMNN) Metric Learning Method...49
4.4 Animal-Like Feature-Shape Classification Using k-Nearest Neighbor (k-NN) Method...54
Ch. 5 Non-Photorealistic Rendering (NPR) Module...57
5.1 Black-and-White Sketch Generation Process...59
5.2 Colored Cartoon-Like Sketch Generation Process...64
5.3 Exaggerative Caricature Creation Using Image Warping Process...69
Ch. 6 Experimental Results...75
6.1 Database Collection...76
6.2 Performance Evaluation of Consistency for Exaggerative Caricature...80
6.3 Performance Evaluation of Recognition Time for Exaggerative Caricature...83
6.4 Performance Evaluation of Consistency for Animal-Like Caricature...85
6.5 Comprehensive Evaluation of ALPP...87
Ch. 7 Conclusions and Future Work...90
References...92
Author’s Biographical Notes...101
Publication List...104

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