Use of wavelet-based basis functions to extract rotation invariant features for automatic image recognition
Use of wavelet-based basis functions to extract rotation invariant features for automatic image recognition
复制标题
使用基于小波的基函数提取旋转不变特征以进行自动图像识别
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
A. Salcedo
中科院分区：
文献类型：
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作者：
S. Akle;M. Algorri;A. Salcedo
在本文中，我们探索使用正交函数作为图像内容的代表性、紧凑描述符的生成器。在图像分析和模式识别中，此类描述符被称为图像特征，它们应该具有一些有用的属性，例如旋转不变性和识别一类图像的不同实例的能力。我们使用 Daubechies 小波族来举例说明我们的算法方法，因为它们形成正交函数集。我们通过使用三组不同的图像特征进行比较 OCR 实验来对生成的图像特征的质量进行基准测试。我们的算法可以使用各种正交函数来生成旋转不变特征，从而提供了识别最适合识别不同类别图像的图像特征集的灵活性。
In this paper we explore the use of orthogonal functions as generators of representative, compact descriptors of image content. In Image Analysis and Pattern Recognition such descriptors are referred to as image features, and there are some useful properties they should possess such as rotation invariance and the capacity to identify different instances of one class of images. We exemplify our algorithmic methodology using the family of Daubechies wavelets, since they form an orthogonal function set. We benchmark the quality of the image features generated by doing a comparative OCR experiment with three different sets of image features. Our algorithm can use a wide variety of orthogonal functions to generate rotation invariant features, thus providing the flexibility to identify sets of image features that are best suited for the recognition of different classes of images.