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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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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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2008年IEEE TRANSACTION关于活动轮廓的经典文献。活动轮廓在图像分割和计算机视觉中有广泛应用，本文提出局部化基于区域的活动轮廓。效果很好。-2008 IEEE TRANSACTION active contour about classic literature. Active contour in image segmentation and computer vision has extensive application of this paper, local regio
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为了减少alpha-expansion算法的计算量，本文在标号为alpha的像素向其它像素膨胀的过程中，先隔离非alpha类间的联系，而只考虑alpha类与非alpha类之间的关系，从而避免alpha-expansion算法需要构造辅助结点的问题，减少了s/t图中边的数目，提高了算法的计算效率。因放松了非alpha类间的关系对alpha膨胀的约束，使得算法可以更容易得跳出能量函数的局部极小点而获得更优的分割结果。-In order to reduce alpha- expansion algor
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边缘是图像中最重要的信息，是模式识别、图像分
割以及场景分析的重要基础。边缘检测是图像处理领
域一项重要的技术和任务。图像中边缘通常解释为灰
度值有突然改变，像素梯度具有局部极大值。很多经
典的边缘检测算子如:Roberts, Sobel, Prewitt, Frei-
Chen和Laplacian等，均以图像局部区域特征与特定
边缘模式匹配为基础，定义数个方向导数模板(一般为
窗口)，将图像与模板进行卷积运算实现边缘检测。它
们具有实现简单，速度快速，易于应用
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边缘是图像中最重要的信息，是模式识别、图像分
割以及场景分析的重要基础。边缘检测是图像处理领
域一项重要的技术和任务。图像中边缘通常解释为灰
度值有突然改变，像素梯度具有局部极大值。很多经
典的边缘检测算子如:Roberts, Sobel, Prewitt, Frei-
Chen和Laplacian等，均以图像局部区域特征与特定
边缘模式匹配为基础，定义数个方向导数模板(一般为
窗口)，将图像与模板进行卷积运算实现边缘检测。它
们具有实现简单，速度快速，易于应用
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CBIR-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 a
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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
i
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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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由于光线分布不均匀或斑块噪音干扰等原因,往往使所要处理的指纹的灰
度值分布缺乏均匀性。在指纹特征自动识别提取过程中,造成许多传统的算法在局部出
现很大的误差。利用方差和均值特征的自动提取方法,首次对不均匀灰度图像进行自适
应分割。然后通过对图像分区域进行不同程度的自适应调整,使具有相同属性的像素单
元具有近似的灰度值分布。调整结果的灰度均匀水平与预先指定的调整精度成正比。这
种调整不仅提高了图像分割的自适应性,而且进一步扩大了一般阈值算法的应用领域。-Gray value d
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This paper addresses the problem of segmenting an image into regions. We de¯ ne
a predicate for measuring the evidence for a boundary between two regions using a
graph-based representation of the image. We then develop an e±cient segmentation
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基于改进的局部二值拟合主动轮廓模型的指静脉图像分割-he improved local two value fitting active contour model for finger vein image segmentation
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Watershed segmentation method is a topology-based theory of mathematical morphology segmentation method, the basic idea is the image as a geodesic topology on the landscape, the image pixel gray value of each point indicates that the point of the alt
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fsdfđ gdWatershed segmentation method is a topology-based theory of mathematical morphology segmentation method, the basic idea is the image as a geodesic topology on the landscape, the image pixel gray value of each point indicates that the poin
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Global and local methods of unsupervised Byesian segmentation of images
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Natural Image Segmentation textured locally melt
Bayesian local segmentation into two classes
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基于OTSU和局部阈值的二次肺实质分割算法，有效的解决了分割不清楚问题，提高了分割质量，且运算简单方便。-Based on OTSU and secondary lung parenchyma of local threshold segmentation algorithm, effectively solve the problem of the division is not clear, improve the quality of the segmentation, and simpl
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This paper advances prior work by proposing a joint learning
framework to simultaneously identify the spatial and temporal
extents of the action of interest in training videos. To get pixel-level
localization results, our method uses dense traj
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提出一种利用视觉显著性对图像进行分割的方法。首先提取图像的底层视觉特征，从局部显著性、全局显著性和稀少性3个方面计算各特征图像中各像素的视觉显著性，得到各特征显著图；对各特征显著图进行综合，生
成最终的综合显著图。然后对综合显著图进行阈值分割，得到二值图像，将二值图像与原始图像叠加，将前景和背景分离，得到图像分割结果-It presents a significant advantage of visual image segmentation method. First, extract
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