查询词典 segmentation-cavity

Eight dimensional feature vectors(3-dimensional coordinate,3-dimensional normal vector and2-dimensional curvature)are taken as input for this fuzzy SOFM to realize segmentation in reverse engineering.

1引言逆向工程中的区域分割是将点云数据按照指定分割准则犤1犦分成若干个子集。

In other words, localization can be performed based on a general scene. In this thesis, we adopt Spatial Gaussian Mixture Model in image segmentation to describe an image viewed by the robot in spatial domain without any artificial landmark. Secondly, we use a modified combination algorithm by Cyr and Kimia to combine similar data. It is found out that a monotonic relationship exists among the scene registered and its neighborhood in terms of distance. This phenomenon can be used to assist to localize the robot and this work demonstrate the feasibility by several experiments.

本论文中采用高斯混合模型(Gaussian Mixture Model， GMM)来描述空间中的场景，将场景概念化，这样任意的场景皆可以用高斯混合模型来表达，而无需加入大量的人工场景，接著将所获取的每个希望校正点的资料采用由修改后的Cyr and Kimia的结合演算法来减低及统合资料，当场景接近原本的当初建立的场景，经由最大可能性大体上会呈现单调的特色，将辅助机器人修正回原本的位置，以达到协助机器人定位的目标。

Data sample pretreatment of decision tree and Support Vector Machine method is conducted by the documents of product design knowledge segmentation processing, feature extraction, feature merger and expression vector.

通过对产品设计知识文档的分词处理、特征抽取、特征合并及向量表达，为决策支持向量机方法提(来源：d6f5A6fBC论文网www.abclunwen.com)供样本数据预处理。

Deformable model is playing a more and more important role in image segmentation owing to its unique advantages.

变形模型凭借其独特的优势，在图像分割领域受到了越来越广泛地研究和应用。

Deformable models ,also known as "snakes", have been one of the most active and successful research areas in image segmentation.

近年来，可变形模型鉴于其良好的性能，在医学图像分割中得到了广泛的应用。

A novel method of content-based image segmentation using deformable template matching is proposed.

本文提出一种采用可变形模板匹配技术进行基于内容的图像分割算法。

In image processing, method as follows will be introduced in this paper: a method combining image threshold iterative segmentation with threshold interpolation, edge detection operator Sobel and LoG, edge linking method using delation operator based on mathematical morphology, using boundary tracking and projection method in edge distill process. Through this method, measurement of wheelset will be met precision demand.

本文阐明了图像的分通道自动采集过程，以及对采集到的原始图像进行预处理过程，达到图像去噪声的目标，本课题采用了阈值分割中迭代阈值和阈值插值相结合的方法，Sobel算子、LoG算子边缘检测算法，基于数学形态学的膨胀运算子进行边缘断点连接以及目标提取中的投影法和边缘跟踪方法，使得提取轮对图像边缘达到测量精度的要求。

Considering the characteristics of detritus image, this article proposed an improved segmentation method based on watershed transformation.

针对岩屑图像的特征，提出了一种改进的分水岭算法分割方案。

Firstly, a quantitative relation about finite deformation and geometrical parameters which exhibit variation of laser spectral image was introduced. And then, particular emphasis was put on some important computer processing steps related to the analysis of spectral image, such as digitizing, segmentation, feature extraction and analytical algorithms. Lastly, some examples about an experiment of a ring subject to large diametral compression were given.

文中首先给出了有限应变和平均整旋角与反映频谱图像变化的几何参数之间的定量关系，然后着重介绍了与频谱图像分析有关的数字化、分割、特征提取和分析算法等重要的图像处理过程，最后给出圆环受径向荷载大变形实验的分析实例。

The applications of the theory in robot translation parameter computation and in Qualitative Scene Model construction are presented.(3) Intrinsic Images and color constancy of Real Color Images Based on the Dichromatic Reflection Model proposed by Klinker [55] , it is the NO. 1 for this paper to measure the spectral relationship of a CCD color camera, to reestablish the linear relationship, to separate the illuminant and object color components in real color images, and to extract the intrinsic image and highlight image from the real color image, as well as to eliminate the influences of highlights upon the usual image segmentation algorithm.

以Klinker[55]等提出的"双色反射模型"为基础，首次测定了彩色摄像机的光强----灰度响应关系，恢复了摄像机的整体线性关系；首次在真实彩色图像（即由彩色摄像机直接获取的图像）中成功地分离了光照和景物的颜色；成功地抽取了本色图像和耀斑图像；在此基础上，又将双色反射模型成功地运用到真实彩色图像的分割方面，消除了耀斑对分割的影响。

Warden: That's a beautiful dog, but don't bring her again.

那是条漂亮的狗，但是不要再把她带来了。

You get up in the morning – no hangover!

你第二天早上起来的时候—不会宿醉！