| Computer Technology |
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| Image segmentation using novel adaptive robust bilateral filter |
| ZHANG Jian ting,ZHANG Li min |
| Naval Aeronautical and Astronautical University, Yantai 264001, China |
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Abstract At first, an efficient and robust bilateral filtering (BF) model was derived from the view of robust regression. Then, a self-adaptive local smoothing scale algorithm was introduced to effectively remove the intensity variations of local image caused by textures and noise. With the new BF model, intensity and texture gradients could be extracted separately from input images and texture features. At last, gradients were fused with morphological method; watershed transform was used to implement image segmentation. Experiments were carried out with Berkeley segmentation database and some remote sensing images. As indicated, the proposed novel adaptive robust bilateral filter method can obtain visual consistent segmentation results. Meanwhile, compared with traditional algorithms, the method can acquire more accurate boundaries and reduce over-segmentation effect, which means that this method can be an effective tool in the preprocessing stage of image analysis．
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Published: 22 September 2016
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新型自适应稳健双边滤波图像分割
从稳健回归分析的角度,推导得到高效的稳健双边滤波(BF)模型.针对由纹理、噪声等导致的图像局部梯度变化差异性,提出一种自适应的局部平滑尺度方法.运用该双边滤波模型处理原图像和纹理特征图,提取出灰度梯度和纹理梯度.采用形态学方法进行梯度融合,并使用分水岭变换实现图像分割.在Berkeley图像数据集和遥感图像上的实验结果表明：所提出的新型自适应稳健双边滤波方法可以得到视觉一致的分割效果;与传统方法相比,提高了对象边界识别率并降低了过分割效应;可以作为图像分析预处理过程的有效工具.
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