本文選題：非真實感繪制　切入點：形態(tài)學細化　出處：《曲阜師范大學》2008年碩士論文　論文類型：學位論文

【摘要】： 隨著計算機圖形學的飛速發(fā)展,以非真實感為目標的圖形學越來越受到人們的重視。非真實感繪制是計算機圖形學中一個嶄新而富有活力的分支。線條畫作為一種有效表示形狀抽象信息的工具,屬于非真實感繪制研究的領(lǐng)域。因其獨特的表現(xiàn)力和抽象性廣泛應(yīng)用于美術(shù)創(chuàng)作、動漫制作及平面廣告設(shè)計等領(lǐng)域。線條是最簡單、最有效的交流方式,也是表達畫者主觀情感的重要手段,線條能夠準確地表現(xiàn)自然物體的特征和輪廓。線條畫的這些特點使我們能夠快速識別和鑒別出圖像特定內(nèi)容而很少受到無關(guān)信息的干擾。另外,基于線條的物體表示還在處理時間和存儲空間方面有很好的優(yōu)勢。 為了得到非真實感圖片,人們研究了各種方法。在這個領(lǐng)域中,如何從圖像中提取能代表該圖像內(nèi)容的線條畫和風格轉(zhuǎn)換是一個研究熱點。本文主要是探索基于圖像的線條畫提取,矢量化控制和風格轉(zhuǎn)換方法。對于藝術(shù)形式的多樣性和復(fù)雜性,如果找到一個合理的表示模型把某種藝術(shù)形式的風格數(shù)量化,風格重用與調(diào)整的問題就好解決了。 本文給出了基于二維圖像自動提取線條畫的處理框架�？蚣苡腥糠纸M成:線條提取,線條繪制和線條風格轉(zhuǎn)換。在線條提取部分中,給出兩種提取圖像內(nèi)容的方法:一是形態(tài)學細化,并給出了一種加速運算的方法。二是依靠經(jīng)典邊緣檢測算子獲得圖像中物體的邊緣信息。在線條繪制部分中,首先把離散像素點用深度優(yōu)先遍歷的方法生成筆劃路徑集合,然后用三次B樣條逼近繪制出每一條筆劃,根據(jù)抽象程度的不同來控制線條,達到不同的繪制效果。風格轉(zhuǎn)換部分是在線條矢量化的基礎(chǔ)上,應(yīng)用平面形狀演化理論,通過對筆劃的幾何屬性進行控制和調(diào)整,提供了一種靈活的調(diào)整線條畫風格的手段,生成了具有夸張風格的線條畫。實驗結(jié)果顯示用本文方法能從圖像中抽象出生動的線條畫,是原圖像內(nèi)容的抽象。 本文主要是探索如何從二維圖像中得到矢量化的線條問題�；诒疚牡墓ぷ�,可以對提取出的線條畫采用樣本學習的方法對線條進行風格轉(zhuǎn)換和定制方面的研究,還可研究其他復(fù)雜藝術(shù)形式的非真實感繪制的模型和方法,從而更好的實現(xiàn)由真實感照片到各種非真實照片藝術(shù)化轉(zhuǎn)化。
[Abstract]:With the rapid development of computer graphics, People pay more and more attention to non-realistic graphics. Non-realistic rendering is a new and dynamic branch of computer graphics. Line painting is an effective tool for representing abstract information of shapes. It belongs to the field of non-realistic rendering. Because of its unique expressiveness and abstractness, it is widely used in the fields of art creation, animation production and graphic advertising design. Line is the simplest and most effective way of communication. It is also an important means of expressing the subjective feelings of the painters. Lines can accurately represent the features and contours of natural objects. These features of line paintings enable us to quickly identify and identify specific contents of an image with little interference from irrelevant information. Line-based object representation also has a good advantage in processing time and storage space. In order to get non-realistic pictures, people have studied various methods. In this field, How to extract line painting and style transformation which can represent the content of the image is a research hotspot. For the diversity and complexity of art forms, if a reasonable representation model is found to quantify the style of a certain art form, the problem of style reuse and adjustment can be solved. This paper presents a processing framework for automatic line drawing based on two-dimensional images. The framework consists of three parts: line extraction, line drawing and line style transformation. This paper presents two methods for extracting image content: one is morphological thinning, and the other is a method of accelerating operation, and the other is to obtain the edge information of objects in the image by classical edge detection operator. Firstly, the discrete pixels are generated by depth-first traversing method, then each stroke is drawn by cubic B-spline approximation, and the lines are controlled according to the different degree of abstraction. On the basis of line vectorization, applying the theory of plane shape evolution, through controlling and adjusting the geometric attribute of stroke, a flexible method of adjusting the style of line drawing is provided. The experimental results show that the method of this paper can abstract the vivid line painting from the image, which is the abstraction of the original image content. This paper is mainly to explore how to get vectorized lines from two-dimensional images. Based on the work of this paper, we can use the method of sample learning to study the style conversion and customization of lines. It is also possible to study the models and methods of non-realistic rendering of other complex art forms, so as to better realize the artistic transformation from realistic photos to various kinds of non-real photos.
【學位授予單位】：曲阜師范大學
【學位級別】：碩士
【學位授予年份】：2008
【分類號】：TP391.41

【引證文獻】

相關(guān)碩士學位論文 前1條

1 吳宗勝;建筑物圖像的風格化增強技術(shù)研究[D];長安大學;2012年

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本文編號：1599039