Description: 形态学分量分析 将图像分为纹理和平滑两部分 直接可以运行-Morphological component analysis for texture and smooth， the image is divided into two parts, can run directly Platform: |
Size: 222208 |
Author:唐东 |
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Description: 形态学成分分析，该方法的基本思想是利用信号组成成分的形态差异，将图像分割为纹理和分片光滑部分。-Morphological Component Analysis，The basic point is that an image can be divided into texture and piecewise smooth parts by the morphological diversity among signal components. Platform: |
Size: 3276800 |
Author: |
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Description: ：为解决现有视频监控系统中目标检测算法无法应付复杂的室外环境且计算量和存储量较大等问题，将像素从RGB 空间转换到YUV
空间建立基于码本的背景模型，并单独对每个码字中的亮度分量进行高斯建模，提取运动目标的轮廓后，用连通区域算法对图像进行形态
学处理。典型测试序列和ROC 数据的对比实验结果证明该算法是高效和实用的，且易于在DSP 或FPGA 等嵌入式系统上实时实现。-】In order to solve the problems that the existing motion detection algorithm in surveillance system can not work well in complex outdoor
scene, and needs too much computation and memory, this paper proposes a moving objects detection algorithm based on improved codebook model.
Pixels are converted from RGB space to YUV space to build the Codebook Model(CBM), and then the luminance component of each codeword is
modeled by Gaussian model. The image is morphological processed by connected components algorithm. A test with the typical video sequences and
the analysis of ROC data prove that the algorithm is effective and practical, and it can easily be implemented in embedded system such as DSP and
FPGA. Platform: |
Size: 92160 |
Author:陈思宇 |
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Description: 基于信号稀疏分解的形态成分分析来进行图像的分解和修复原作者的英文原文献-Morphological component analysis based on the signal sparse decomposition of the image decomposition and restoration of the original author of the original English literature Platform: |
Size: 2738176 |
Author:kjl |
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Description: 实现MCA（形态分量分析）算法，实现了点状和线状目标的分离，本人特地总结出来，简单明了，各个功能里面有描述-Achieve the MCA (morphological component analysis) algorithm, punctate and linear target separation, I specially summed up, plain and simple, each function is described inside Platform: |
Size: 768000 |
Author:丁宪成 |
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Description: 背景:快速的将心脏按其特征进行聚类可为后续统计分析和研究带来很大的便利.系统聚类法是将样品或变量按照其性质上的亲疏相似程度进行分类的一种多元统计方法.目的:提出用主成分一聚类分析的方法来描述心脏形态学形状并进行分类,对中国健康成年人的心脏X射线测量的各项指标进行综合评价.方法:搜集了36例健康成年人的胸片,并用MxLiteView软件手动测量了每幅胸片中代表心脏形态学形状常用的10个指标,用Matlab软件对测量指标进行主成分分析,然后对提取出的主成分进行聚类.结果与结论:主成分分析后提取出3个主成分变量,将36例样本用提取的主成分进行聚类,可将样本分为3类,分别代表了心脏的3类不同的心型.用该方法对心脏形态学形状进行快速分类,对心脏的统计和分类研究提供了一定的参考价值.-Background: The heart of their fast clustering features can bring great convenience for subsequent statistical analysis and research system clustering method is variable according to the sample or the nature of the closeness of the similarity of a multivariate statistical classification. methods Objective: proposed using principal component analysis of a clustering method to describe the shape of cardiac morphology and classification of the indicators Chinese healthy adult heart X-ray measurement of comprehensive evaluation methods: collected 36 cases of healthy adult person s chest and measuring MxLiteView software manual chest represents the heart of each piece of morphological shape commonly used 10 indicators to measure using Matlab software principal component analysis and principal components of the extracted clustering results aND CONCLUSION: after principal component analysis to extract the three main components of the variables, the 36 cases with samples extracted principal co Platform: |
Size: 666624 |
Author:王斌 |
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Description: 图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。
传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利
用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological
Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解
为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得
到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点-Image inpainting is to fill the missing data in corrupted images and thus to reduce
the information loss of damaged image. Traditional inpainting algorithms are dependent on specific
structure of target images compressive sensing theory makes is possible to realized image
inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD
and MCA algorithm, which first decomposes the image into texture part and structure part, and
then trains the two dictionaries for these two parts with KSVD and reconstructs the original image
with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better
adaptability and performance as compared with traditional algorithms. Platform: |
Size: 687104 |
Author:孙红娟 |
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Description: mca形态学成分分析，将图片分为纹理部分和结构部分-MCA morphological component analysis, the image is divided into the texture part and the structural part Platform: |
Size: 2048 |
Author:吕柏霖 |
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Description: 基于稀疏分解的形态学成分分析，在分解图像的同时完成了去噪任务。(Based on the morphological component analysis of sparse decomposition, the image is decomposed and the denoising task is completed at the same time.) Platform: |
Size: 29696 |
Author:JunL10 |
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Search - morphological component analysis