This paper proposes a moving car segmentation method based on improved OTSU method.
提出一种基于改进OTSU方法的运动车辆分割算法。
Otsu method is a common and efficient image segmentation algorithm, and has been used in many various real-time systems.
最大类间方差法是一种常用而有效的图像分割算法，并已在许多实时系统中采用。
Based on the Rough Set Theory, the image data is simplified and classified into targets and backgrounds with the combination of the OTSU method.
在图像中利用粗糙集理论对图像特征数据进行有效约简，并和最大类间方差法结合，将图像分为目标和背景两部分。
The comparison between this method and the 1-dimensional OTSU method show that this method performs much better when the images are corrupted by noise.
通过与一维的Otsu法比较表明，在有噪声的图象中，本文提出的方法性能好得多。
The method avoids large amount of variance calculation and its calculation quantity is only about 50% that of Otsu method. It can fulfill the demands of adaptability and real time.
该方法避免了大量的方差计算，计算量仅为大津法的50%左右，能够满足自适应性和实时性的要求。
One of the most useful thresholding techniques using gray-level histogram of an image is the OTSU method. The objective of this paper is to extend it to the 2-dimensional histogram.
Otsu法是最常用的利用图象一维灰度直方图的阈值化方法之一，本文的目的是将它推广到二维直方图。
One of the most useful thresholding techniques using gray-level histogram of an image is the OTSU method. The objective of this paper is to extend it to the 2-dimensional histogram.
Otsu法是最常用的利用图象一维灰度直方图的阈值化方法之一，本文的目的是将它推广到二维直方图。
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