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在这个例子中我会画一个柱状图。In this case, I'm going to plot a histogram.

首先，对图像进行直方图均衡化处理。First, the image histogram equalization of treatment.

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计算各区域内的颜色统计直方图，进行二类颜色聚类。Bi-color clustering based on the color histogram was performed.

其中灰度直方图法是本系统告警信号灯识别的最佳方法。The gray histogram arithmetic is the best method for this system.

YUV图像改进处理器基于直方图修改。YUV picture improvement processor based on histogram modification.

您可以选择用报告、图表或者直方图来显示这些值。You can show them as a report, as a graph over time, or as a histogram.

这个例子演示了折线图、柱状图和饼状图的绘制方法。This example demonstrated a line chart, histogram and pie mapping methods.

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场景变化检测算法是基于直方图之差的。The algorithms of scene change detection are based on histogram differences.

给出了一个改进的直方图均衡化图象增强算法。This paper develops an image enhancement algorithm of histogram equalization.

过曝相片的直方图，可以看到直方图在右侧“溢出”表明相片有一部分“过曝”。A histogram for a under exposed image. Here is pixels are"spilling"off the left end.

任何过程，“延伸”的直方图有可能导致色调分离。Any process which "stretches" the histogram has the potential to cause posterization.

该方法明显优于经典的同态增晰法和直方图均衡化。The method is better than traditional homomorphic filtering and histogram equalization.

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病房关闭色调分离的最佳方法是保持任何直方图操纵到最低限度。The best way to ward off posterization is to keep any histogram manipulation to a minimum.

应力范围直方图可用作估算等效均方根应用力范围。The stress range histogram was used to estimate the effective root-mean-square stress range.

常规直方图均衡化增强后的图像在高灰度区有过亮的感觉。The image enhanced by traditional histogram equalization is over-bright in the high gray area.

直方图以图形的方式总结和显示一个数据集的分布。A histogram is used to graphically summarize and display the distribution of a process data set.

相反的，如果直方图集中在右侧，说明图像过曝。Conversely, if the histogram skews towards the right, then the image is potentially overexposed.

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如果你发现直方图集中在左侧，说明图像太暗了，或者说曝光不足。If you notice that histogram skews to the left, the image is probably too dark, or underexposed.

第二阶段采用统计直方图聚类削波求取阈值，并得到最终准确的端点。In the second phase, the threshold and the exact result is got by the statistics histogram method.

图像的直方图双峰不明显时，选择分割阈值就很困难。It is difficult to choose the threshold value when the histogram of images is not evidently bimodal.