The image threshold segmentation method of pests in stored grain is a base and very important part.
阐述了储粮害虫图像分割是储粮害虫智能检测识别系统的基础和关键。
The image threshold segmentation algorithm based on the Particle Swarm Optimization (PSO) combined with the rough entropy based on boundary region is presented.
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。
The optimum threshold determined by the theory of image segmentation is to be the crossover point of the fuzzy enhancement in order to enhance the contrast of image.
利用图像分割原理确定的最佳阈值作为模糊增强的分界点，完成对图像的对比度增强。
By using threshold segmentation, edge recognition, pixel labeling and center of gravity method, the accurate sites of every model can be acquired from the image.
综合运用阈值分割、边缘识别和像素标记法等图像识别技术，通过图像得到每个模型的相关定位信息。
This system detected motion target using difference image method through threshold image segmentation, then automatically tracked target by using centroid tracking algorithm.
该系统运用差分图像法，通过阈值图像分割来检测运动目标，并采用形心跟踪算法来自动跟踪目标。
Based on maximum between-cluster variance method and uniformity measure, this paper USES maximum entropy principle to select the gray-level threshold value for image segmentation.
在最大类间方差法和一致性准则法的基础上，运用最大熵原理来选择灰度阈值对图像进行分割。
This method splits difference image into a set of subimages, and determines segmentation threshold of the whole scene by combining the thresholds of subimages.
该方法将差异图像分割成子图像，通过求子图像的局部阈值来确定差异图像的整体阈值。
The paper firstly discussed two ways for image segmentation:image segmentation by global threshold and image segmentation by dynamic threshold.
首先讨论了图像阈值分割的两种方法：全局阈值图像分割和动态阈值图像分割。
According to the characteristics of radar image, a method of image segmentation is suggested by performing threshold processing directly after edge detection instead of close curves.
根据雷达图像的特点提出边缘检测后不做曲线闭合，而直接进行阈值处理的图像分割。
A knowledge based image segmentation with multi threshold fusion is presented.
提出了一种基于知识的多阈值融合图像分割新方法。
Binaryzation image is obtained through adaptive threshold segmentation and noise is removed by sorting method.
采用自适应门限进行阈值分割，得到二值化的图像；利用聚类的方法去掉噪声点。
The first method is adaptive threshold segmentation based on local blocks of image.
第一种图像分割方法是基于局部分块的图像自适应阈值分割方法。
The optimum threshold determined by the theory of image segmentation is to be the crossover point of the fuzzy enhancement, in order to enhance the contrast of image.
利用图像分割原理确定的最佳阈值作为模糊增强的分界点，完成对图像的对比度增强。
This paper presents a quick object image segmentation algorithm based on adaptive threshold against infrared object images in complex background.
针对复杂场景中的红外目标图象，本文提出了一种基于自适应阈值面的目标图象快速分割算法。
The threshold methods of image segmentation are surveyed in this article, especially those based on the gray level histogram.
本文对现有的各种图像阈值分割方法进行综述，重点介绍了基于图像灰度直方图的阈值分割方法。
Finally, the output of this algorithm is the optimal threshold. Using this threshold to partition off the pixels, image segmentation is implemented.
最后，通过寻优搜索得到算法的输出值即为最优阈值，以此阈值划分像素，实现图像分割。
And the neighborhood averaging, threshold choice and image segmentation in the image processing are adopted in the system.
该系统采用了图象处理中的邻域平均法、阈值选取及图象分割技术。
Image threshold technology is the most commonly used method of image segmentation because its principle is very simple and easy to implement.
图像的阈值分割技术由于其原理简单，易于实现，已成为目前最常用的图像分割方法。
The most basic principle in image segmentation is to determine the threshold, and use it to divide different pixel into different categories, to achieve image segmentation.
图像分割的最基本的原理是确定阈值，并由阈值将图像中的不同像素归为不同的类别，完成图像的分割。
In image segmentation algorithms, the selection of optimal threshold is the key to segmentation.
在阈值分割算法中，确定最优阈值是图像分割的关键。
The adaptive threshold method of image segmentation is adopted to extract the candidate targets from the fusion image.
然后对时空域融合的目标增强图像进行自适应阈值分割来提取弱小目标。
Base on the recognition arithmetic, a segmentation method by forecasting threshold is proposed. Using this segmentation method in image tracking, satisfying effect is gotten.
在目标特征提取识别算法的基础上，还提出了阈值预测分割算法，并应用在序列图像的跟踪中，取得了较好效果。
In infrared image segmentation processing, the selection of a threshold has been a hot issue.
在红外图像的分割处理中，阈值的选取一直是研究的热点。
OTSU threshold method has a very wide range of applications in the image segmentation for its simplicity and intuitive.
OTSU阈值方法以其简洁性和直观性在图像分割中有着十分广泛的应用。
Combining the two essential characteristics of the image, the gradient and the gray level, a threshold segmentation approach using maximum entropy with the gradient boundary control was proposed.
结合梯度和灰度这两种图像的本质特征，提出一种基于边界梯度控制的最大熵阈值分割方法。
The optimum threshold value was obtained and accurate segmentation of hydrophobic image was realized, which can meet the need of the classification of hydrophobicity levels based on BP neural network.
采用基于最大类间方差的遗传分割法通过反复迭代搜索得到较优的分割阈值，实现了憎水性图像的准确分割，可满足后续基于BP神经网络的憎水性等级的判定。
In view of the characteristics of infrared image, the veracity is not good when the commonly used threshold is used in infrared image segmentation.
常用的阈值分割方法在对红外图像进行分割时，由于红外图像本身的特点，会出现准确性不高的问题。
In view of the characteristics of infrared image, the veracity is not good when the commonly used threshold is used in infrared image segmentation.
常用的阈值分割方法在对红外图像进行分割时，由于红外图像本身的特点，会出现准确性不高的问题。
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