Edge detection is an essential step in image analysis and recognition, and it is an important technology in the image preprocessing procedure.
边缘检测是图像分析识别必不可少的环节，是一种重要的图像预处理技术。
The area of image is calculated by using edge tracing and vector analysis in this paper. The result is available in analysing practical biomedical and metallurgical images.
通过对边界的跟踪与矢量分析，实现了对面积的计算。实际用于生物医学、金相等图像分析，结果表明该方法可行。
The third part USES wavelet analysis and Mathematical Morphology to detect the image edge, and contrasts the two results, we have gotten respective apply condition and good or evil.
第三部分分别使用小波分析和数学形态学进行了数字图象边缘检测，并将两者的检测结果进行了对比，得出了各自的适应条件和优劣之处。
The area of image is calculated by using edge tracing and vector analysis in this paper.
通过对边界的跟踪与矢量分析，实现了对面积的计算。
After the wavelet's multi-resolution analysis, a feature fusion approach was adopted to enhance remote sensing image edge and improve the definition and resolving power of the image.
在数字图像小波多分辨率分析理论基础上，采用小波变换方法对高分辨遥感图像的目标地物边缘进行信息增强，然后与多光谱遥感图像进行特征信息融合。
The edge detection of SAR is very important for image analysis and recognition.
在对SAR图像的分析和识别中，边缘的检测信息十分重要。
The edge information of cell image is very useful, which is the basis of cell image segment, it can affect the correctness of cell image recognition and analysis directly.
细胞图像中的边缘信息是极为有用的，它是进一步进行细胞图像分割的基础，直接影响到细胞图像识别与分析的正确性。
And we analysis the effect that the difference color element have on edge detection and image segmentation, which shows blue element is the most effective.
同时还分析了不同颜色分量对边缘检测及图像分割的影响，结果表明使用蓝色分量效果最好。
Location and edge detection of the target object is crucial in the medical image processing and it is the base of quantitative analysis and aid diagnosis.
在医学图像的处理中，目标对象的定位和边缘提取至关重要，它是定量分析和辅助诊断的基础。
In transient state signal and in image analysis, the point of discontinuity often is one of important characteristics, they are located the edge of the important structure frequently.
在瞬态信号与图像的分析中，突变点往往是重要的特征之一，它们常常位于重要结构的边缘部分。
Aiming at the disadvantage of fuzzy image, by making some analysis about extracting edge feature based on fuzzy contrast enhancement algorithm, two images registration is achieved.
针对模糊图像的弱点，分析了通过增强象素模糊属性对比度来提取边界特征的基本原理，实现了两幅图像的配准。
By applying wavelet analysis method to image edge detection, multi scale operator of wavelet analysis functions well for noise resisting ability and picture edge reserving.
将小波分析方法应用于图像边缘检测领域，小波分析的多尺度算子在抗噪声和保留图像边缘的能力上有比较好的效果。
So the cell identification and quantitative analysis for the micro image is realized by performing circular arc algorithm to the cell edge.
对此细胞边缘运用圆弧算法实现显微图像的细胞识别和定量分析。
Edge detection is an important step in digital image analysis.
边缘检测是数字图像分析中重要的一环。
The first always is edge detection in image comprehend and analysis. It is one of most activity topic in machine-vision filed, and it has very important status in project application.
图像理解和分析的第一步往往就是边缘检测，目前它已成为机器视觉研究领域最活跃的课题之一，在工程应用中占有十分重要的地位。
Edge is an important attribute for image analysis and recognition, but it is difficult to effectively extract edge from noisy image in the domain.
景物边缘信息是进行图象分析和识别的重要属性，如何有效地从噪声图象中提取边缘是这些领域中的难点。
The edge is also an important feature of the image and its detection results directly affect the SAR image recognition and analysis.
而边缘也是图像的重要特征，其检测效果直接影响SAR图像的识别和分析。
In this paper, the image edge detection and image edge trace algorithms used to processing and analysis arthroscopic image are discussed.
主要论述了采用图像边缘检测和图像跟踪算法对采集的关节镜图像进行图像处理和分析的过程。
Finally, it gave an image de-noising algorithm of coherence enhancing diffusion, which used wavelet coefficients to estimate the image edge according to the wavelets time-frequent analysis function.
针对相干增强扩散计算扩散矩阵较慢的缺点，提出了一种用小波系数估计图像边缘方向的相干增强扩散图像降噪算法。
Edge detection is one of the base contents on image processing and analysis, on which people have not as yet find a satisfactory way out.
边缘检测是图像处理与分析中最基础的内容之一，也是至今仍没有得到圆满解决的一类问题。
Edge detecting is a fundamental issue in image analysis and image processing.
边缘提取是图像处理与图像分析的基础。
Our analysis shows that directional wavelet transforms can better reflect the edge information of images because they better corresponds to the characteristics of image direction and texture.
比较了方向小波变换和传统小波变换在图像边缘检测中的不同之处。分析得出方向小波变换更符合图像的方向、纹理特征，因而更能反映图像的边缘信息。
In this paper, a new multifractal algorithm is proposed based on sub-pixel edge measure. The method can precisely divide the image from texture to edge for image singularity analysis.
本文提出了一种基于亚像素边缘测度的多重分形算法，应用于图像奇异性分析中可以实现从纹理到边缘各层面的精确划分。
Then we use the technology of image analysis and image division based on the edge to extract the main object and to recognize (classify) the visual characteristic.
然后利用图像分析技术，采用基于边缘的图像分割方法，提取图像的主体区域并进行视觉特征提取与（分类）识别。
How to extract the edge effectively in noise image is a difficult problem in the field of image analysis. The traditional methods used to extract edge are often very sensitive to noise.
在噪声图像中如何有效的提取边界是图像分析中的难点，常用的边界提取方法往往对噪声很敏感。
Image edge detection is one of basic problems in image processing, and is a key step in image processing and image analysis.
图像边缘检测是图像处理的基本问题之一，是图像处理到图像分析的关键步骤。
The image edge-enhanced technique was proposed to improve the automatic aligning efficiency and aligning accuracy in the X-ray lithography aligning system, and the numerical analysis was given.
提出了用于提高X射线光刻对准系统自动对准效率和对准精度的图像边缘增强技术，并进行了数值分析。
Detecting edge from noisy images is a key step for image test and analysis.
从被噪声干扰的图象中提取边界是图象测试与分析的关键之一。
And image edge detection is the basic step in image processing and analysis.
图像边缘检测是图像处理，图像分析等的基本步骤。
This paper use the multiscale wavelet analysis to filter the noise, then use wavelet transform local maximum in the detection to edge of image.
当图像数据混有噪声的时候，对边缘的检测比较困难。
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