形状分类的多尺度傅里叶描述子

I. Kunttu, Leena Lepistö, J. Rauhamaa, A. Visa
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引用次数: 49

摘要

物体形状的描述是图像的一个重要特征。在图像处理和模式识别中,使用了几种不同的形状描述符。在人类的视觉感知中,形状以多种分辨率进行处理。因此,在基于形状的图像分类和检索中,多尺度形状表示至关重要。在物体形状的描述中,多分辨率表示还为形状分类提供了额外的准确性。提出了一种新的形状分类描述符。这个描述符被称为多尺度傅里叶描述符,它结合了傅里叶描述符和多尺度形状表示的优点。这个描述符是通过对目标边界的小波变换的系数进行傅里叶变换而形成的。通过这种方式,傅里叶描述符可以以多种分辨率表示。我们使用三个图像数据库进行分类实验。将本文方法的分类结果与傅立叶描述子的分类结果进行了比较。
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Multiscale Fourier descriptor for shape classification
The description of object shape is an important characteristic of an image. In image processing and pattern recognition, several different shape descriptors are used. In human visual perception, shapes are processed in multiple resolutions. Therefore, multiscale shape representation is essential in shape based image classification and retrieval. In the description of an object shape, the multiresolution representation provides also additional accuracy to the shape classification. We introduce a new descriptor for shape classification. This descriptor is called the multiscale Fourier descriptor, and it combines the benefits of a Fourier descriptor and multiscale shape representation. This descriptor is formed by applying a Fourier transform to the coefficients of the wavelet transform of the object boundary. In this way, the Fourier descriptor can be presented in multiple resolutions. We performed classification experiments using three image databases. The classification results of our method are compared to those of Fourier descriptors.
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