本文提出一种基于图像邻域信息的分割方法。
An image segmentation algorithm based on contextual information is proposed in this paper.
利用像素的邻域信息来去除由于摄像机抖动和场景小运动产生的噪声。
Pixel's neighbor information is considered to remove noise due to camera jitter and small motion in the scene.
本文提出了一种基于高斯混合模型邻域信息融合的海面运动目标检测算法。
This paper proposes a new method of tracking the moving maritime objects in video sequences based on neighboring information fusion using Gaussian mixture model.
使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。
For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.
针对带钢表面缺陷图像去噪后边缘模糊的问题，提出了一种基于邻域信息的自适应双倒数滤波方法。
According to neighborhood information an adaptive double-reciprocal filter was proposed to improve the edges which were degraded by denoising.
本文利用改进的模糊聚类算法，依据邻域信息实现了对丢失图像信息的恢复，并完成了对该图像的检测。
In this paper, an improved FCM algorithm (CFCM) is given, through which the lost information of in-complete-data images can be restored, while edge-detection of images is accomplished.
为了克服传统FCM算法的局限性，本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法（2DFCM）。
In order to overcome the limitation of FCM, a novel Two-dimension Fuzzy Cluster Method (2DFCM) was proposed based on the spatial information.
本文选取细节点邻域的方向场，细节点，纹线三方面的信息全面地描述细节点的邻域信息，并将特征以三值特征向量的形式存储。
In our algorithm, the direction of minutiae neighborhood field, neighborhood minutiae, neighborhood ridges are used as descriptions. and all descriptions are stored as three-valued feature vectors.
模糊映射是图像模糊处理的基础，目前主要是基于图像象素值的映射，没有充分利用图像的邻域相关信息。
Fuzzy mapping is the basis of image fuzzy processing. But it is now, primarily based on the pixel value, without the regard of neighboring information.
利用中心像素邻域灰度信息，提出一种新的图像边缘宽度细化算法，使斜坡边缘的细化效果有明显提高。
A new algorithm of ramp width reduction based on the gray information of neighborhood pixels is proposed in order to sharpen the ramp edge effectively.
再对候选点进行基于灰度投影积分的小邻域微调；然后结合灰度信息和边缘信息进行人眼点的筛选。
After the dots are fine tuning based on gray projection calculation, they are chosen ground on gray and edge information.
本文主要讨论遥感图象空间邻域结构信息分析方法。
This paper is about the remote sensing image analysis using spatial contextual in-formation.
算法利用伪随机数产生经过模型中心的直线，选取以直线与模型交点为球心的球形邻域作为嵌入对象，通过抖动调制邻域内顶点的重心来嵌入水印信息。
Firstly, a group of lines through the model center depending on a pseudorandom number is generated. Then the intersection points of these lines and the model's surface are chosen as embedding objects.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
结构张量包含有图像的邻域结构特征，借助这些信息可在降噪的同时保持图像边缘的细节特征。
The structure tensor contains neighborhood structure features of image. It can preserve the details of images edge as well as image de-noising.
利用兴趣点进行序列图像匹配可以利用兴趣点及其邻域的灰度信息和兴趣点集的几何信息。
The information available for image matching includes the gray-level of or near interest points and the geometrical relation of interest points.
结合粗糙集理论和遥感数据中地物光谱特征空间分布信息，提出了一种基于光谱特征邻域的容差粗糙集分类方法，用来处理卫星遥感数据分类中的不确定性问题。
Based on the spectral feature neighborhood, this paper proposes a tolerant rough set classification method to handle the uncertainty in the process of satellite remote sensing data classification.
结合粗糙集理论和遥感数据中地物光谱特征空间分布信息，提出了一种基于光谱特征邻域的容差粗糙集分类方法，用来处理卫星遥感数据分类中的不确定性问题。
Based on the spectral feature neighborhood, this paper proposes a tolerant rough set classification method to handle the uncertainty in the process of satellite remote sensing data classification.
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