本文選題：主動表觀模型　切入點：人臉卡通化　出處：《電子科技大學》2013年碩士論文　論文類型：學位論文

【摘要】：人臉的多樣性和獨特性一直是計算機視覺、圖形學和模式識別等領(lǐng)域研究的熱點問題；卡通化藝術(shù)以其特有的表現(xiàn)形式和手法引領(lǐng)著藝術(shù)發(fā)展的新潮流；兩者相結(jié)合的人臉卡通化通過線條的描繪和顏色的渲染，以其特有的夸張模式，形象和逼真的再現(xiàn)人臉圖像，在網(wǎng)絡(luò)游戲、互動論壇、社交軟件以及動漫等領(lǐng)域應(yīng)用廣泛。 現(xiàn)有的基于學習的人臉卡通化方法通常將圖像分成若干小塊，，并通過對圖像塊的匹配及合成，來實現(xiàn)卡通圖像的合成。然而受到塊效應(yīng)的影響，圖像中的人臉特征細節(jié)描述不詳細，人臉線條效果欠佳。而目前流行的人臉定位方法，通過對人臉特征的準確定位，能夠得到更好的細節(jié)描述及線條效果。 本文主要研究對于一幅給定的人臉圖像計算機如何自動地生成具藝術(shù)家繪畫作品特定風格的卡通人臉圖像，以及對卡通人臉進行表情動畫變換。主要的研究內(nèi)容如下： 一、提出了兩種基于學習的卡通人臉圖像生成算法：基于參數(shù)模型的卡通化方法和基于特征點的卡通化方法�；趨�(shù)模型的卡通化方法是在主動表觀模型的基礎(chǔ)上，通過學習人臉匹配的過程，利用參數(shù)估計的方法生成卡通人臉圖像；基于特征點的卡通化方法則從高層語義學的角度出發(fā)，將人臉特征分類處理，通過分別合成卡通人臉頭發(fā)、輪廓和五官，實現(xiàn)卡通人臉圖像的生成。 二、為了得到較好的彩色卡通效果，采用三種顏色渲染方法對卡通圖像上色，分別為基于顏色空間轉(zhuǎn)換的方法和基于圖像分割的方法�；陬伾臻g轉(zhuǎn)換的著色方法得到的卡通圖像色調(diào)與輸入圖像相似，而基于圖像分割的著色方法得到卡通圖像與藝術(shù)家繪畫的色彩風格更相近。 三、在生成的卡通圖像的基礎(chǔ)上進行面部表情的變換。利用圖像變形算法，通過控制主要面部器官特征點的位置，使無表情的卡通人臉變換出微笑、悲傷等形象、生動和俏皮的表情。 實驗證明，本文提出的人臉卡通化方法能夠獲得較理想的卡通效果，并且卡通人臉的表情動畫生動、形象，時效性高。
[Abstract]:The diversity and uniqueness of human face has always been a hot topic in computer vision, graphics and pattern recognition, and the art of Katonghua leads the development of art with its unique forms and techniques. The combination of the two face cards through the description of lines and color rendering, with its unique exaggeration mode, image and lifelike reproduction of face images, in online games, interactive forums, social software and animation and other fields widely used. The existing learning-based face card generalization methods usually divide the image into several small blocks and realize the cartoon image synthesis by matching and synthesizing the image blocks. However, due to the influence of block effect, The detail description of face features in image is not detailed, and the effect of face line is not good. However, the popular face localization method can get better detail description and line effect through accurate location of face features. This paper mainly studies how a given face image computer can automatically generate a cartoon face image with a specific style of the artist's painting work, and how to make a cartoon facial expression animation transformation. The main research contents are as follows:. Firstly, two learning based cartoon face image generation algorithms are proposed: one is based on parametric model and the other is based on feature point, and the other is based on active apparent model. By learning the process of face matching, we use the method of parameter estimation to generate cartoon face image. Based on feature point, we classify the face features from the perspective of high-level semantics, and synthesize the hair of cartoon face separately. Contour and facial features to achieve cartoon face image generation. Secondly, in order to get a better color cartoon effect, three color rendering methods are used to color the cartoon image. The color space conversion method and the image segmentation method are used respectively. The color color method based on color space conversion is similar to the input image. The coloring method based on image segmentation shows that the color style of cartoon image is more similar to that of artist painting. Third, on the basis of the generated cartoon image, the facial expression is transformed. By controlling the location of the feature points of the main facial organs by using the image deformation algorithm, the expressionless cartoon faces are transformed into images of smile, sadness, etc. A vivid and playful expression. Experimental results show that the proposed method can obtain ideal cartoon effect, and the facial expression of cartoon face is vivid, vivid and time-efficient.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.41

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