Matching Handwritten Line Drawings with Von Mises Distributions
Matching Handwritten Line Drawings with Von Mises Distributions
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DOI：
10.1587/transinf.e94.d.2487
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发表时间：
2011-12
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影响因子：
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通讯作者：
Katsutoshi Ueaoki;Kazunori Iwata;N. Suematsu;A. Hayashi
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文献类型：
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作者：
Katsutoshi Ueaoki;Kazunori Iwata;N. Suematsu;A. Hayashi
二维形状通常用数字图像中的线条图或物体轮廓来表示。形状可以分为两种类型，即有序形状和无序形状。有序形状是点的有序集合，而无序形状是无序集合。因此，每种类型通常使用不同的属性来定义表示从形状采样的点的局部分布所涉及的局部描述符。在本文中，我们重点关注无序形状。由于无序形状的大多数局部描述符不是尺度不变的，因此在应用形状匹配过程之前，我们通常通过尺度归一化使图像数据集中的形状具有相同的大小。如果原始整体形状相似，则通过尺度归一化获得的形状适合此类描述符。但是，如果每个原始形状的部分使用不同的比例绘制，则它们不适合。因此，在本文中，我们提出了一种由冯·米塞斯分布构造的尺度不变描述符来处理此类形状。由于该描述符具有尺度不变和概率分布的优点，因此它不需要尺度归一化，并且可以在匹配形状点中采用概率分布的任意度量。在形状匹配和检索的实验中，我们展示了我们的描述符与几种传统描述符相比的有效性。
A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.