本文關(guān)鍵詞： 形狀分析 特征提取 凸度衡量 形狀分類 三維模型檢索　出處：《華東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：形狀分析是計算機(jī)視覺領(lǐng)域里的一個熱門研究方向,并在模式識別、圖形標(biāo)注、形狀分解、圖像配準(zhǔn)等領(lǐng)域得到廣泛應(yīng)用。而形狀提取的過程中容易出現(xiàn)扭曲、遮擋、噪聲等干擾,所以提出一種既滿足平移、縮放、旋轉(zhuǎn)不變性又對噪聲有較高魯棒性的形狀描述符具有重要的理論意義和實際意義。目前緊密度、線性度、矩形度、凸度等幾何特征得到了國內(nèi)外的廣泛研究,其中凸度表示物體的凹凸程度,是一種全局幾何特征,因其具有顯著的視覺特性,在視覺感知中扮演著重要的角色�，F(xiàn)有的凸度測量方法可以分為三類:基于面積的方法、基于邊界的方法和基于概率的方法。其中,基于面積的方法以其計算簡便、抗噪性強(qiáng)等優(yōu)點被廣泛的使用。然而現(xiàn)有的基于面積的方法由于只考慮凹陷面積和凸包面積的大小,導(dǎo)致其測得的許多結(jié)果并不合理。本文提出了一種基于面積的二維凸度衡量方法,它是對原始基于面積的方法的一種改進(jìn)。本文假設(shè)所有非凸的形狀都是由其凸包不斷凹陷所致,不同的凹陷方式會對原形狀產(chǎn)生不同的影響力,如果凹陷對原形狀有較大的影響力,則形狀具有較低的凸度值,反之,則形狀具有較大的凸度值。我們采用了一種距離權(quán)重積分(Distance Weighted Area Integral,簡稱DWAI)的方法來計算凹陷的影響力,根據(jù)每個點與形狀的凸包中心(Geometric Center of ConvexHull,簡稱GCCH)的距離來分配這個點的影響力,離GCCH較遠(yuǎn)的點具有較低的影響力,即在形狀的凸包邊緣的凹陷區(qū)域具有較低的影響力。通過改變參數(shù)影響因子可以調(diào)整區(qū)域的影響力,增大影響因子可以增大凹陷位置帶來的影響,反之則減小影響,當(dāng)影響因子等于0的時候,本文的方法退化為原始的基于面積的方法,即凹陷的位置不影響凸度的計算,所以本文的方法可以完全取代原始的基于面積的方法。其次,通過把DWAI的思想拓展到三維空間,本文提出了一種三維凸度衡量方法,視覺感知上比現(xiàn)有最新的Lian的方法更加合理。根據(jù)此方法,通過設(shè)置不同的參數(shù),提取統(tǒng)計信息,本文設(shè)計了一種基于凸度的三維模型形狀描述符CS(Convexity Statistic),與Lian的基于凸度的形狀描述符CD(Convexity Distribution)相比,CS總體上具有更好的檢索效果。本文通過理論證明驗證了所提方法的有效性,大量的實驗結(jié)果表明,本文的方法在定性和定量兩方面都優(yōu)于其他對比方法。
[Abstract]:Shape analysis is a hot research field in the field of computer vision, and has been widely used in the fields of pattern recognition, graphics annotation, shape decomposition, image registration and so on. Therefore, a shape descriptor which not only satisfies translation, scaling, rotation invariance but also has high robustness to noise has important theoretical and practical significance. At present, the degree of compactness and linearity is very important. Rectangle, convexity and other geometric features have been widely studied at home and abroad, in which convexity represents the concave and convex degree of objects, which is a global geometric feature, because of its obvious visual characteristics. It plays an important role in visual perception. The existing convex measurement methods can be divided into three categories: area-based method, boundary-based method and probability-based method. The area-based method is widely used because of its simple calculation and strong anti-noise. However, the existing area-based methods only consider the size of the concave area and convex hull area. As a result, many of the results obtained are unreasonable. In this paper, an area based two-dimensional convexity measurement method is proposed. It is an improvement on the original area-based method. In this paper, it is assumed that all non-convex shapes are caused by continuous indentation of its convex hull, and different indentation modes have different effects on the original shape. If the depression has a greater influence on the original shape, the shape has a lower convexity value and vice versa. We adopt a distance Weighted Area Integral. The method of DWAI is used to calculate the influence of the depression, according to the center of the convex hull of each point and shape of the geometric Center of ConvexHull. The distance between GCCHs to distribute the influence of this point, the point farther away from the GCCH has lower influence. The influence of the region can be adjusted by changing the parameter influence factor, and the influence of the depression location can be increased by increasing the influence factor. When the influence factor is equal to 0, the method in this paper degenerates into the original area-based method, that is, the position of the depression does not affect the calculation of the convexity. Therefore, this method can completely replace the original area-based method. Secondly, by extending the idea of DWAI to three-dimensional space, this paper proposes a three-dimensional convexity measurement method. Visual perception is more reasonable than the latest Lian method. According to this method, the statistical information is extracted by setting different parameters. In this paper, a 3D model shape descriptor based on convexity, CS(Convexity Statistics, is designed. Compared with Lian's Convexy-based shape descriptor CD(Convexity Distribution. In this paper, the effectiveness of the proposed method is proved by theory. A large number of experimental results show that the proposed method is superior to other comparative methods in both qualitative and quantitative aspects.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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