研究了一种噪声条件下的分割系统

R. Qureshi, Xiaobo Li, A. Sather
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引用次数: 0

摘要

本文报道了一种用于生猪超声图像腰部检测的图像分割系统。图像具有低对比度,高水平的噪声,以及在纹理和形状方面的高度差异。我们的分割算法从一个区域生长过程开始,它提供了一个粗略的近似腰部区域。形态学操作和曲线拟合消除了不必要的噪声。最后,一个活动轮廓处理细化了结果区域的形状。该方法不依赖于纹理或对比度的特定先验信息。该系统基于模块化设计原则,可以方便地将不同的区域生长和细化算法作为模块替换到当前的设计中。因此,该系统的通用性足以适应其他涉及低对比度图像的分割任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a system for segmentation under noisy conditions
This paper reports on an image segmentation system primarily developed for detecting loins in ultrasonic images of live pigs. The images have a low contrast, high level of noise, and a high degree of variance in terms of texture and shape. Our segmentation algorithm starts with a region growing process, which provides a rough approximation of the loin region. Morphological operations and curve fitting eliminate unwanted noise. Finally, an active contour process refines the shape of the resulting region. This method does not rely on specific a priori information of the texture or the contrast. The system is based on the principle of modular design, so that different region growing and refinement algorithms can be easily substituted into the current design as modules. Therefore, the system is general enough to be adapted to other segmentation tasks involving low contrast images.
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