Object detection using Gabor filters
Object detection using Gabor filters
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DOI：
10.1016/s0031-3203(96)00068-4
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发表时间：
1997-02-01
影响因子：
8
通讯作者：
Lakshmanan, S
中科院分区：
文献类型：
--
作者：
Jain, AK;Ratha, NK;Lakshmanan, S
本文涉及到复杂背景中的目标检测。一个基于特征的分割方法的对象检测问题的追求，在多个空间方向和频率计算的功能。该方法进行如下：一个给定的图像是通过一个银行的偶对称Gabor滤波器。对这些滤波图像进行选择，并且对每个（所选择的）滤波图像进行非线性（S形）变换。然后，在每个变换图像像素周围的窗口中计算纹理能量的度量。纹理能量（“Gabor特征”）及其空间位置被输入到平方误差聚类算法。该聚类算法产生原始图像的分割-它为图像中的每个像素分配聚类标签，该聚类标签识别像素在不同空间方向和频率上拥有的平均局部能量的量。该方法被应用到一些视觉和红外图像，其中每一个包含一个或多个对象。对应于对象的区域通常被正确地分割，并且“Gabor特征”的唯一签名通常与包含感兴趣对象的片段相关联。实验结果表明，这种对象检测方法在一些问题域的有用性。这些问题出现在NHS、军事侦察、指纹分析和图像数据库查询中。版权所有（C）1997模式识别学会。
This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (''Gabor features'') and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image - it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of ''Gabor features'' is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in NHS, military reconnaissance, fingerprint analysis, and image database query. Copyright (C) 1997 Pattern Recognition Society.