指纹图像细化算法的研究。
指纹图像细化后处理。
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.
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.
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.
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.
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.
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.
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.
其中图像预处理中的图像增强是最为关键的环节，直接影响着后面指纹图像的细化特征提取等处理。
A critical step is the fingerprint enhancement that affects the process of thinning and minutia extraction directly.
二值化和细化是指纹图像预处理阶段比较重要的环节，本文讨论了多种细化算法的优劣，并通过编码实现了这些算法。
Binary and thinning is more important in fingerprint image pre-processing, this paper discussed the pros and cons of various thinning algorithms and through coding implemented these algorithms.
在二值化中采用局部自适应方法与传统的固定阀值方法作比较，细化中运用了数学形态学的方法，对指纹图像进行细化。
We compare local self-adaptive way with the fixed value in binarization, and use mathematical morphology method to thin image.
在二值化中采用局部自适应方法与传统的固定阀值方法作比较，细化中运用了数学形态学的方法，对指纹图像进行细化。
We compare local self-adaptive way with the fixed value in binarization, and use mathematical morphology method to thin image.
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