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Gaussian and Gabor Filter Approach for Object Segmentation

机译:高斯和伽柏滤波的目标分割方法

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摘要

The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA)for object segmentation. An improved and efficient approach based on Gaussian and Gabor Filter reads the given input image and performs filtering and smoothing operation. The region occupied by the object is extracted from the image by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing. The proposed approach experimented on standard images taken from Caltech datasets, Corel Photo CDs. and Weizmann horse datasets show significantly improved results.
机译:提出的用于对象分割的高斯和Gabor滤波方法(GGFA)解决了从背景分割对象的问题。一种基于高斯和Gabor滤波器的改进高效方法,读取给定的输入图像并执行滤波和平滑操作。通过执行诸如双边滤波,边缘检测,聚类和区域增长之类的各种操作,可以从图像中提取出对象所占据的区域。提议的方法在从Caltech数据集Corel Photo CD拍摄的标准图像上进行了实验。和Weizmann马数据集显示出明显改善的结果。

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