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本文对几何模型匹配方法进行了研究,提出了一套完整的人脸定位算法。在预处理部分,采用了特别的增强人脸特征与脸部皮肤之间对比度的方法及局域取阈值二值化方法,改进了预处理的效果。在图像分割部分,实现了经典的分合算法,并且使用成组算法改进了分合的效果。在人脸匹配部分,实现了基于眼睛和嘴的几何模型匹配,并对评价函数的构造进行了研究-geometric model of this matching method for the study and submit a complete set of facia
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文章介绍了合成孔径雷达图象处理的各种方法.斑点噪声的去除,图象分割的算法,以及压缩,去噪等内容.大家可以共同参考.-article on the synthetic aperture radar image processing of the various methods. Speckle removal, Image segmentation algorithms, and compression, such as denoising. We can common reference.
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本书详细介绍了用VC++进行数字图像处理的方法,包括图像获取、图像增强、图像分割等内容-This book gives a detailed introduced by vc++ for digital image processing methods, including image acquisition, image enhancement, the content such as image segmentation...
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Shadow detection and removal is an important task when dealing with colour outdoor images. Shadows are
generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of
light that reaches a surface.
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图像分割中的马尔可夫随机场方法综述,中国图象图形学报上面的文章,2007年-Image segmentation of Markov random field methods, the Chinese Journal of Image and Graphics of the above articles, 2007
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介绍了基于C均值聚类和基于核函数的C均值聚类两种图像分割的方法-Introduced based on the C-means clustering, and Kernel-based C-means clustering two kinds of image segmentation methods
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本文主要针对图像分割方法进行了研究综述说明。-In this paper, image segmentation methods for research synthesis note.
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对现有的用于部分图像分割的水平集方
法进行了综述,主要介绍传统水平集方法、无重新初始化水平集方法、连续水平集方法以及最近相关的改进方法,并简要讨论了各种方法的优缺点以及应用情况,最后指出了水平集方法进一步研究的方向-The existing image segmentation for part of the level set square
Method were summarized, mainly introduces the traditional level set metho
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Two novel contributions to Content Based Image Retrieval are presented and
discussed. The fi rst is a search engine for font recognition. The intended usage
is the search in very large font databases. The input to the search engine is an
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Image Segmentation.
A central problem, called segmentation, is to distinguish objects from background [10]. For intensity images (ie, those represented by point-wise intensity levels) four popular approaches are: threshold techniques, edge-based
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对图像分割方法进行描述,对各种分割方法进行总结讲解,讲解区别特点-Described method for image segmentation, segmentation methods to summarize the various explanations, to explain the difference between features
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Abstract—Noninvasive ultrasound imaging of carotid plaques
allows for the development of plaque-image analysis methods associated
with the risk of stroke. This paper presents several plaqueimage
analysis methods that have been developed over th
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algorithm of watershed by immersion vincent and soille and others methods to segmente an image
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Basic Idea of Image Segmentation
All the image segmentation methods assume that:
1. the intensity values are different in different regions, and,
2. within each region, which represents the corresponding object in a
scene, the intens
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提出了一种复杂背景下的多车牌图像分割和识别方法,首先采用统计和特征匹配相结合的方法进行背景提取,将可能存在车辆的区域提取出来;然后分别对可能的车辆区域进行局部边缘检测,并使用车牌的先验知识确定车牌的位置和单个字符分割,包括车牌倾斜时的字符分割;最后使用PCA和神经网络相结合的方法精确识别车牌。-Proposed a multi-plate image segmentation and recognition method under a complex background, the first
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Semi-automatic watershed medical image segmentation methods
for customized cancer radiation treatment planning simulation
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This the MATLAB code that was used to produce the figures and tables in Section V of
F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference
in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on
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介绍图像分割的几种不同方法,有很好的学习参考价值-Several different methods of presentation of image segmentation, there is a good learning reference value
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彩色图象的特征提取与计算软件设计说明书。说明书中介绍了一些彩色图象的特征提取方法,包括颜色、纹理、特征点的提取,另外还介绍了几种图像分割的方法。-This is a color image feature extraction and calculation software design specification.This paper based on color and texture, methods of extracting feature points, and some imag
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图像阈值法分割综述:图像分割是一个经典的难题,由于图像分割的复杂性,图像分割的方法很多也很难有一个通用的方法。该文主要是
时图像阈值法分割的一个综述,叙述了阈值法分割的一些方法。最后对这些算法做了一个总结,以及时阂值法分割的期望-Image thresholding segmentation Roundup: Image segmentation is a classic problem, due to the complexity, image segmentation method fo
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