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自己翻译的一段话，问题有点多啊，求助大神帮忙，可追加金币啊,真的很着急！
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| Threshold processing is a kind of regional segmentation technology. It mainly use the gray-scale differences between the target object and the background of images to choose a suitable threshold. By judging the characteristics of （each pixel attributes） in the image whether satisfy the threshold requirements to determine the pixel of the image belong to the target area or the background area. Thereby resulting in a binary image, the object image and the background image has a strong contrast. The core issue of the threshold segmentation is how to choose a suitable threshold, therefore, how to select threshold value has become the hotspot in image processing, and put forward a variety of threshold selection methods. Wu et al. proposed a mathematical morphology method, erosion operation that in Suggestions were put forward minimum cost function under the premise of iterative automatic segmentation algorithms. Reference Wu's algorithm, in this paper, combined the best iterative thresholding algorithm and the corrosion algorithm to segment tissue cell microscope image. |
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Threshold processing is a kind of regional segmentation method. A suitable threshold can be chosen based on the gray-scale differences between the target object and the background in the image. By judging whether each pixel attribute satisfies the threshold requirements, one can determine the pixel of the image belongs to the target area or the background area. This results in a binary image, the object image and the background image with a strong contrast. The core issue in threshold segmentation is how to choose a suitable threshold value, which has also become the hotspot in image processing, and led to a variety of threshold selection methods. Wu et al. proposed a mathematical morphology method. They suggested the erosion operation with minimum cost function under the premise of iterative automatic segmentation algorithms. In this paper, we proposed the best iterative thresholding algorithm and the corrosion algorithm to segment the microscopic image of histocyte combined with Wu's algorithm. |
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xiuzi073116楼2014-08-30 22:06:22
自私的猫1988
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2楼2014-08-30 14:50:38
自私的猫1988
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3楼2014-08-30 14:54:57
自私的猫1988
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4楼2014-08-30 14:58:16
5楼2014-08-30 14:59:22
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It mainly use the gray-scale differences between the target object and the background of images to choose a suitable threshold. By judging the characteristics of each pixel attributes in the image whether satisfy the threshold requirements to determine the pixel of the image belong to the target area or the background area. Thereby resulting in a binary image, the object image and the background image has a strong contrast. 它主要通过图像的前景和背景之间的灰度差来选取一个合适的阈值。通过判断图像中每个像素的特征是否满足阈值要求来决定这个像素是属于前景还是背景。从而得到一个前景和背景有着强烈反差的二值图像。 |
6楼2014-08-30 15:25:25
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Wu et al. proposed a mathematical morphology method[12], erosion operation that in Suggestions were put forward minimum cost function under the premise of iterative automatic segmentation algorithms. Reference Wu's algorithm, in this paper, combined the best iterative thresholding algorithm and the corrosion algorithm to segment tissue cell microscope image. Wu等提出了数学形态学、腐蚀运算在建议代价函数最小前提下的迭代自动分割算。参考Wu的算法，本文结合迭代最佳阈值法和腐蚀算法对组织细胞显微镜图像进行分割。 |
7楼2014-08-30 15:26:21
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现在只需要修改“Wu et al. proposed a mathematical morphology method[12], erosion operation that in Suggestions were put forward minimum cost function under the premise of iterative automatic segmentation algorithms. Reference Wu's algorithm, in this paper, combined the best iterative thresholding algorithm and the corrosion algorithm to segment tissue cell microscope image. Wu等提出了数学形态学、腐蚀运算在建议代价函数最小前提下的迭代自动分割算。参考Wu的算法，本文结合迭代最佳阈值法和腐蚀算法对组织细胞显微镜图像进行分割。” 谢谢各位了啊 金币全给了 还可追加 |
8楼2014-08-30 17:23:35
自私的猫1988
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9楼2014-08-30 19:24:16
10楼2014-08-30 19:28:50