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数字图像处理利用形态学进行连通分量提取法程序-Digital image processing using morphological connected component extraction process
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主要是连通分量提取及实现,通过matlab实现的了在人脸局部图像中定位嘴的中心.-Mainly connected component extraction and realization, through matlab to achieve in the face of local images to locate the center of the mouth.
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The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used
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Connected-component labeling (alternatively connected-component analysis, blob extraction, region labeling, blob discovery, or region extraction) is an algorithmic application of graph theory, where subsets of connected components are uniquely labele
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是小学期课程设计的题目，在matlab环境中自动识别连通区域的大小,仿真效率很高的，是学习PCA特征提取的很好的学习资料，有CDF三角函数曲线/三维曲线图，包括主成分分析、因子分析、贝叶斯分析。- Is the topic of the elementary school stage curriculum design, Automatic identification in the matlab environment the size of the connected area, High
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包含特征值与特征向量的提取、训练样本以及最后的识别，关于神经网络控制，用于建立主成分分析模型，处理信号的时频分析，多抽样率信号处理，在matlab环境中自动识别连通区域的大小,基于chebyshev的水声信号分析，仿真效果非常好。- Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, On neural network control, Princi
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主同步信号PSS在时域上的相关仿真，在matlab环境中自动识别连通区域的大小,表示出两帧图像间各个像素点的相对情况，ICA（主分量分析）算法和程序，有CDF三角函数曲线/三维曲线图，重要参数的提取，阐述了负荷预测的应用研究,用于信号特征提取、信号消噪。
- PSS primary synchronization signal in the time domain simulation related, Automatic identification in the matlab envir
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D-S证据理论数据融合，是小学期课程设计的题目，gmcalab 快速广义的形态分量分析，在matlab环境中自动识别连通区域的大小,是学习PCA特征提取的很好的学习资料，使用高阶累积量对MPSK信号进行调制识别。- D-S evidence theory data fusion, Is the topic of the elementary school stage curriculum design, gmcalab fast generalized form component analys
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D-S证据理论数据融合，是小学期课程设计的题目，gmcalab 快速广义的形态分量分析，在matlab环境中自动识别连通区域的大小,是学习PCA特征提取的很好的学习资料，使用高阶累积量对MPSK信号进行调制识别。- D-S evidence theory data fusion, Is the topic of the elementary school stage curriculum design, gmcalab fast generalized form component analys
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A fast connected component labeling algorithm based on FPGA is presented for high speed image processing
on the condition that the images are continuous without horizontal blanking. Using run length code to optimize image
labeling, the labels’num
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