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有意义区域
相关语句
  meaningful regions
     Meaningful Regions Extraction Based on Image Analysis
     基于分析的图像有意义区域提取
短句来源
     A new method for meaningful regions segmentation in natural color image is proposed in this study.
     提出一种自然彩色图像中有意义区域的分割方法 .
短句来源
  meaningful region
     Image Segmentation for Meaningful Region Based on Color and Texture
     基于颜色和纹理特征提取彩色图像的有意义区域
短句来源
     Meaningful Region Segmentation of an Image Combined with Intensity Distribution and Spatial Information Field
     结合图像灰度信息和空间信息的有意义区域分割
短句来源
  “有意义区域”译为未确定词的双语例句
     Meaningful regions-based algorithm for color image retrieval
     基于有意义区域的颜色检索算法
短句来源
     An Arithmetic of the Semantic-Based Image Region Extraction
     一种基于语义的图像有意义区域提取算法
短句来源
     Those images with high value were split into sub-regions by image segmentation,and others were just split into sub-blocks. Each block was regarded as a region,and was given a dynamic weight.
     为此,提出了一种基于有意义区域的颜色检索算法,即首先计算图像的颜色聚合度,据此确定图像分割方法,对聚合度较高的图像利用区域生长方法进行分割,对聚合度较低的图像进行简单的分块,提取各区域的颜色特征并动态加权求和进行检索。
短句来源
     And according to result of the experiment,we can get the conclusion that this arithmetic ensures the accuracy of the meaning image region.
     实验结果证明该方法具有较好的提取效果,降低了提取结果的信息冗余,保证了图像中有意义区域提取的准确性。
短句来源
  相似匹配句对
     Meaningful regions-based algorithm for color image retrieval
     基于有意义区域的颜色检索算法
短句来源
     Meaningful Regions Extraction Based on Image Analysis
     基于分析的图像有意义区域提取
短句来源
     Outline of regional geological setting
     区域地质概况
短句来源
     The Regional Dimension
     关于区域
短句来源
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  meaningful regions
One of the fundamental problems in computer vision is the segmentation of an image into semantically meaningful regions, based only on image characteristics.
      
As in the study of -meaningful regions, some care must be taken of notation and abbreviations.
      
Bottom-left, barycenters of all meaningful regions whose area is inside the only maximal meaningful mode of the region areas histogram.
      
It has been suggested that knowing the image motion would facilitate segmentation of the scene into physically meaningful regions.
      
It can be seen that the proposed segmentation scheme has succeeded is creating meaningful regions without blocky contours.
      
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  meaningful region
Evaluating this probability using the torus quantization conditions shows that in the physically meaningful region, where a closed convergence of rays covers the caustic circle, the probability is greater than unity.
      
A maximal meaningful region discards all its parent regions but the branches issued from this parent regions also have to be explored.
      
The probability of a segment being an anatomically meaningful region increases with its homogeneity, compactness and size.
      


To support semantic based image retrieval, the meaningful regions in image need to be first extracted. This paper proposes a new scheme for roughly extracting meaningful regions from image based on the results of image analysis, which uses various and suitable techniques according to the characteristics of images. The particularities of this scheme include mainly non supervision and self adaptation. On the basis of analyzing the isolation parameters and coarseness parameters of image clustering, this scheme...

To support semantic based image retrieval, the meaningful regions in image need to be first extracted. This paper proposes a new scheme for roughly extracting meaningful regions from image based on the results of image analysis, which uses various and suitable techniques according to the characteristics of images. The particularities of this scheme include mainly non supervision and self adaptation. On the basis of analyzing the isolation parameters and coarseness parameters of image clustering, this scheme makes use of either thresholding technique based on hue histogram or clustering technique with texture and hue density functions or clustering technique with brightness and hue density functions. Experiments on extracting meaningful regions from a number of real images and on providing the basis for further semantic based image retrieval show some satisfactory results and the potential on real applications.

提出一种基于对图像的分析 ,根据图像特点采取不同方法进行图像有意义区域粗略提取的新方案 .该方案的特点是无监督和自适应 ,在分析图像聚类孤立性参数和粗糙度参数的基础上 ,分别采用色度直方图阈值分割、纹理和色度密度函数聚类、亮度和色度密度函数聚类等不同算法 .对真实图像进行区域提取和将其用于语义图像检索的实验效果表明该方案可满足实际要求

This paper presents a non parametric region competition scheme which combines scale space clustering and region competition to segment the image. It also proposes a formal and general procedure to automatically find the initial regions. Our algorithm can segment an image into regions which are not homogeneous in the sense of statistics, but homogeneous in the sense of semantics with respect to the segmentation context. We call it semantically homogeneous segmentation of the image. Using both semantic homogeneity...

This paper presents a non parametric region competition scheme which combines scale space clustering and region competition to segment the image. It also proposes a formal and general procedure to automatically find the initial regions. Our algorithm can segment an image into regions which are not homogeneous in the sense of statistics, but homogeneous in the sense of semantics with respect to the segmentation context. We call it semantically homogeneous segmentation of the image. Using both semantic homogeneity and quantitative control of the number of the resultant homogeneous regions, our algorithm may produce a 'clean' resultant image, thus simplifying the following procedures.

提出了一种新的图像分割框架——非参数化区域竞争算法 .这种算法克服了基于尺度空间滤波的特征空间聚类法的缺陷 ,提高了原区域竞争算法的性能 ,并且采取了一种自动选取种子位置及大小的形式化策略 .非参数化区域竞争算法可以把图像分割成统计意义上并不具有一致性 ,但在应用中更有意义的区域 ,称这样的分割为语义一致 (或均匀 )的分割 .非参数化区域竞争算法把定量地控制分割结果中的区域个数和语义一致的分割结合起来 ,从而净化了分割结果 ,并且可以降低后继算法的复杂度

In this paper, we propose a new color image segmentation method, which bases on union probability density of hue, light and saturation. We also demonstrate the performance of the proposed method on a variety of images. The experimental results show the performance proposed method is robust.

本文提出了一种基于色调、饱和度和亮度联合概率分布的彩色图象分割方法 .该方法首先将 RGB图象转换为HSL图象 ,并以色调为主要依据对图象进行粗分割 ,然后 ,利用亮度和饱和度信息进行细节分割并组合成有意义的区域 .实验表明该方法的分割效果是好的 .

 
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