本文關(guān)鍵詞： 特征提取 不變矩 幾何特征 奇異值 NMI 小波矩　出處：《廣西師范大學(xué)》2010年碩士論文　論文類型：學(xué)位論文

【摘要】： 剪紙是我國歷史悠久的傳統(tǒng)民間藝術(shù)之一,隨著我國動(dòng)漫產(chǎn)業(yè)的不斷發(fā)展,剪紙藝術(shù)作品將是一種很好的動(dòng)漫素材。在數(shù)字媒體中去實(shí)現(xiàn)這種藝術(shù)形式,首先將剪紙藝術(shù)的作品轉(zhuǎn)換為計(jì)算機(jī)可以儲(chǔ)存的數(shù)字圖像,進(jìn)一步生成相應(yīng)的作品,剪紙紋樣在這些處理過程中有非常重要的作用,它決定了最終剪紙圖像的藝術(shù)形態(tài)。不同剪紙紋樣的精確分類與識(shí)別是剪紙紋樣圖像應(yīng)用的基礎(chǔ),本文對(duì)于剪紙紋樣的識(shí)別進(jìn)行了研究,提出了適合于具有藝術(shù)夸張變形這種獨(dú)特藝術(shù)形式的識(shí)別算法,并取得了較好的識(shí)別效果。 論文的工作主要從以下幾個(gè)方面展開： (1)研究剪紙圖象的特點(diǎn),建立了剪紙紋樣庫。對(duì)采集到的剪紙紋樣圖像進(jìn)行了預(yù)處理,消除噪聲,對(duì)圖象進(jìn)行分割,從復(fù)雜的剪紙圖像中分離出單個(gè)紋樣。通過分析剪紙紋樣的特征,結(jié)合代數(shù)、幾何、統(tǒng)計(jì)等方法,建立了包含有63幅訓(xùn)練樣本和350幅待識(shí)別分類的測(cè)試樣本的剪紙紋樣庫,這些紋樣涵蓋了剪紙藝術(shù)創(chuàng)作中的基本紋樣,為后續(xù)的紋樣識(shí)別工作做準(zhǔn)備。 (2)提出一種基于不變矩與幾何特征的紋樣識(shí)別算法,本文使用傳統(tǒng)的七個(gè)不變矩作為剪紙紋樣的特征向量,具有平移、旋轉(zhuǎn)和尺度不變性。同時(shí)又提取了紋樣圖像的六個(gè)幾何不變特征,利用這兩種不變量分別作為特征向量,采用LM算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò),通過歸一化后的特征向量對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,應(yīng)用訓(xùn)練后的神經(jīng)網(wǎng)絡(luò)作為分類器對(duì)剪紙紋樣進(jìn)行模式識(shí)別,試驗(yàn)證明該方法能夠較好的識(shí)別有一定藝術(shù)變形的剪紙紋樣。(3)將小波分析與其他一些特征提取方法進(jìn)行了結(jié)合。小波分析具有多分辨率的特性,如果能夠在小波多分辨率的基礎(chǔ)上去提取圖像的特征,那么將會(huì)提高特征向量對(duì)圖像自身的表征能力。本文分別在小波分析的基礎(chǔ)上提取了能量特征,奇異值特征,NMI特征和小波矩特征,并做了對(duì)比識(shí)別試驗(yàn)。小波能量方法根據(jù)小波不同分辨率下小波系數(shù),使用能量的表示方式將低頻分量和不同方向的高頻分量表述出來,作為識(shí)別的特征向量。奇異值和NMI方法主要應(yīng)用小波變換提取剪紙紋樣圖象的低頻分量并進(jìn)行奇異值分解或NMI提取,最后通過對(duì)特征值進(jìn)行歸一化和降維處理作為最終的特征向量。這種結(jié)合的方法有效的利用了小波多分辨率的特征,消除了噪聲的干擾,同時(shí)又保持了奇異值和NMI特征的自身特點(diǎn),且該方法計(jì)算簡(jiǎn)單,易于實(shí)現(xiàn)。小波矩特征具有較強(qiáng)的細(xì)節(jié)把握能力和抗噪聲能力,通過對(duì)剪紙紋樣圖像提取小波矩,來獲取圖像的多尺度特征。利用不同特征分量的均值和標(biāo)準(zhǔn)差,來實(shí)現(xiàn)N類模式的特征選擇。實(shí)驗(yàn)證明該方法能夠有效地去除噪聲干擾,較好的識(shí)別有一定藝術(shù)夸張變形的剪紙紋樣。 大量實(shí)驗(yàn)證明,以上方法能夠很好的識(shí)別具有一定夸張變形的剪紙紋樣圖像,在算法的復(fù)雜度方面也能夠滿足計(jì)算機(jī)系統(tǒng)的實(shí)時(shí)要求,為下一步的剪紙圖像的自動(dòng)生成奠定了基礎(chǔ)。
[Abstract]:The paper-cut is one of traditional folk art with a long history in China, with the continuous development of China's animation industry, paper-cut works will be a good cartoon material. To achieve this art form in digital media, first convert the paper-cut works for digital images can be stored on the computer, generate further work the paper-cut patterns, has a very important role in these processes, it determines the final paper-cut image of the art form. The accurate classification and identification of different patterns is based on patterns of image application, this paper does research on the identification of paper-cut patterns, put forward suitable recognition algorithm with artistic exaggeration of this unique art form, and have achieved good recognition effect.
The work of this paper is mainly carried out from the following aspects:
(1) to study the characteristics of paper-cut images, set up paper-cut patterns library. Preprocessing of paper-cut patterns image acquisition to eliminate noise of image segmentation, isolated from the patterns of complicated paper-cut image. Through the analysis of characteristics of the paper-cut patterns with algebra, geometry, statistics, set up 63 training samples and 350 testing samples for identification and classification of the paper-cut patterns libraries contain these patterns, covering the basic patterns of paper-cut art creation, prepare for pattern recognition in the future.
(2) proposed a pattern recognition algorithm based on moment invariants and geometric features, this paper uses seven traditional invariant moments as feature vector, the paper-cut patterns with translation, rotation and scale invariance. While extracting the six geometric patterns of image invariant features, the use of these two kinds of invariants are used as feature vectors, using LM algorithm to optimize BP neural network, BP neural network trained by normalized feature vectors, using the trained neural network as classifier for pattern recognition of paper-cut patterns, the experiment proves that the method can identify better have some artistic deformation patterns. (3) the wavelet analysis are combined with some of the other the feature extraction method. Wavelet analysis has the characteristics of multi-resolution, if it can be based on wavelet multi-resolution image feature extraction, it will enhance the eigenvectors of Characterization of image itself. This paper based on the wavelet analysis to extract the energy feature, singular value feature, NMI feature and wavelet moment features, and do a comparison recognition test. The wavelet energy method according to different wavelet resolution wavelet coefficients, using the energy representation of low-frequency and high-frequency components will be expressed in different directions out, as the feature vector identification. NMI method and singular value mainly used wavelet transform to extract the low-frequency components of the image of paper-cut patterns and singular value decomposition or NMI extraction, by the end of the eigenvalue normalization and dimension reduction as the final feature vector. This method combined with the effective use of wavelet multiresolution. To eliminate the noise, while maintaining the singular value and NMI features, and the method is simple, easy to implement. Wavelet moment feature has Strong ability to grasp the details and anti noise ability, through the paper-cut patterns image extraction of wavelet moments, to obtain multiscale image features. By using the mean and standard deviation of different characteristic components, to achieve N class model feature selection. The experimental results show that this method can effectively remove noise, better identification of exaggeration deformation patterns.
A large number of experiments have proved that the above methods can well identify the image of paper cut pattern with exaggerated deformation, and it can also meet the real-time requirements of computer system in terms of algorithm complexity, which lays the foundation for the next generation of automatic image generation of paper cutting.

【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：TP391.41

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 邵丹;清代家具蝙蝠裝飾紋樣造型藝術(shù)研究[D];東北林業(yè)大學(xué);2012年

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本文編號(hào)：1532994