指纹图像细化算法的研究。
最后对二次增强图像细化。
指纹图像细化后处理。
因此，研究图像细化对提高指纹识别系统的性能具有重要意义。
Therefore, research of image thinning is important to improve the performance of fingerprint recognition system.
该文首次提出了如何用PCNN的脉冲传播特性进行二值图像细化的新方法。
This paper first brings forward a new approach for binary image thinning by using the pulse transmission attribute of PCNN.
实验结果表明，使用改进的OPTA算法，指纹图像细化效果有明显的提高。
Experimental results indicate that the improved OPTA thinning algorithm is better in fingerprint image thinning.
通过多图像平均，图像细化，取样分析，快速方便地测量计算了电子的荷质比。
The charge-mass ratio can be measured expediently by multiple image averaging, fringe thinning and sample analyzing.
对OPTA算法进行了改进，将其应用在指纹图像细化中，并对其局限性进行了分析。
The OPTA thinning algorithm is improved and applied to image thinning, at the same time, its limitation is analyzed.
指纹图像细化是自动指纹识别的关键技术之一，图像细化效果对自动指纹识别系统的性能有着重要的影响。
Fingerprint thinning is a key part in the automatic fingerprint identification technology. The fingerprint thinning result has an important influence of the AFIS.
图像细化是指纹预处理技术中的一个重要环节，大多数指纹识别算法都是在细化图像上实现特征点的检测。
Image thinning is one of important preprocessing procedures for fingerprint images. Most of fingerprint recognition algorithm checks the special points by image thinning.
指纹图像二值化是指纹细化处理的前提，是指纹识别预处理的一个重要部分。
Fingerprint image binarization, as one key step of fingerprint image preprocessing, is the precondition of fingerprint image thinning.
可用于图像的粗化与细化，同时作为图像开发的程序框架也是不错的选择！
Images can be used for coarse and refined, as part of the development process images framework is a good choice!
本文提出了一种基于图像标记的并行细化算法。
A new parallel thinning method based on image marking is proposed in this paper.
该文首先介绍了驾驶员预警系统的原理结构，然后讨论了驾驶员预警系统中人脸图像识别预处理的滤波和细化两个环节。
The principle framework of driver forewarning system is introduced firstly. then two sections of pre-processing namely the filtering and making thinner are discussed.
根据上述分析，本论文提出了基于阶梯细化的图像放大算法。
According to the above analyses, a hybrid algorithm for image zooming based on stair thinning is proposed to decrease the artifacts.
针对提取出的圆形印鉴印文缺损，本文建立了三个基准图像，运用形态学的细化算法及膨胀运算对其进行处理，找到缺损位置并进行补偿。
In order to repair the seal character defect, we establish three benchmarks image to find the defect's location and repair it using thinning and expansion algorithm.
利用这种改进算法，能够同时检测出四个方向的图像边缘，并可以对图像边缘进行细化。
The four orientation information can be detected with the improved algorithm, and the image edge can be thinned.
指纹图像的预处理又可以分为灰度图滤波去噪、二值化、二值化图像去噪、细化和细化后去噪五个部分。
Fingerprint image pre-processing has five parts: filtration in gray-scale image, binarization, filtration in binary image, thinning and filtration in thinning image.
然后根据这一设想，设计相关指纹图像预处理和细化、特征点提取、比对论证等算法。
Base on this assume the fingerprint image's pretreatment and single line characteristic dot pick-up compare and so on arithmetic are designed.
最后对阈值分割后的二值图像进行细化处理，把目标从背景中较为完整的提取出来。
After thinning of the binary images, the targets have been completely extracted from the background.
在指纹门禁系统中，指纹图像预处理构成了整个系统的基础，而二值化与细化则是预处理过程的核心。
In the fingerprint entrance control system, the preprocessing of fingerprint images is the foundation of the whole system whose core is binarization and thinning.
分析并研究了可用于指针图像处理的图像分割、图像滤波及图像的膨胀细化等运算方法。
The method of image threshold, image filter, image dilation and image thinning that can be used to the pointer image procession is analyzed and studied.
对于某些细化后会产生空洞、断点的原始图像，需要对细化后的图像运用闭运算进行填充以保持细化后图像的连通性。
In some cases, the original images will produce cavities and breakpoints after, it needs to be processed by closing method in order to keep the connectivity of thinning image.
指纹图像预处理的研究包含四个方面：图像分割、图像增强、二值化和细化。
In preprocessing of fingerprint image, four sides are discussed, including outline segmentation, filtering enhancement, segmentation binarization and thinning.
通过对采集到的被测工件图像进行边缘检测、细化处理和霍夫变换，从而确定出其角度。
An Angle value is obtained when the measured work piece image is processed by edge detection, thinning and Hough transformation.
最后根据SAR图像的统计性质，利用基于混合模型估计的分类后验概率将初始分割结果逐尺度进行细化得到SAR图像的最终分割。
Third, the initial segmentation is refined scale by scale to get the final segmentation of the SAR image based on the posterior probability of classification which is estimated by the mixture model.
对其进行分割获得不同纹理区域之间的低定位精度的边界围道，再利用原始图像对围道进行高精度细化。
Based on the coarse border, a higher accuracy result can be obtained by taking the original image into account.
对其进行分割获得不同纹理区域之间的低定位精度的边界围道，再利用原始图像对围道进行高精度细化。
Based on the coarse border, a higher accuracy result can be obtained by taking the original image into account.
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