Gabor在图像分析中的特点

J. Kamarainen
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引用次数: 50

摘要

在计算机视觉和图像分析的应用中,Gabor滤波器在特征提取中保持了近三十年的流行。引起人们注意的最初原因是Gabor过滤器与视觉皮层中简单细胞的接受野之间的相似性。一个更实际的原因是他们在许多应用中取得了成功,例如,人脸检测和识别,虹膜识别和指纹匹配,其中基于Gabor特征的方法是表现最好的。通过信号处理的基本领域:空间(时间)和频率,Gabor特征的推导是优雅的。在许多实际和计算优势的基础上,我们将在未来的应用中看到它们的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gabor features in image analysis
In applications of computer vision and image analysis, Gabor filters have maintained their popularity in feature extraction for almost three decades. The original reason that draw attention was the similarity between Gabor filters and the receptive field of simple cells in the visual cortex. A more practical reason is their success in many applications, e.g., face detection and recognition, iris recognition and fingerprint matching, where Gabor feature based methods are among the top performers. The derivation of Gabor features is elegant through the fundamental domains of signal processing: space (time) and frequency. Topped with many practical and computational advantages we will see their use also in future applications.
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