Description: 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. Platform: |
Size: 1024 |
Author:tianwenxu |
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Description: 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 for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
-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 for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
Platform: |
Size: 41984 |
Author:Lee Kurian |
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Description: 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 labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation.
This code is used to separate the connected components in the image. Platform: |
Size: 28672 |
Author:ahmed |
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Description: 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 simulation efficiency, Is a good learning materials to learn PCA feature extraction, There CDF trigonometric curve/3D graphs, Including principal component analysis, factor analysis, Bayesian analysis. Platform: |
Size: 5120 |
Author:tbqhdq |
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Description: 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, Principal component analysis model for establishing, When processing a signal frequency analysis, Multirate signal processing, Automatic identification in the matlab environment the size of the connected area, Based chebyshev underwater acoustic signal analysis, Simulation of the effect is very good. Platform: |
Size: 12288 |
Author:npwaxafz |
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Description: 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 environment the size of the connected area, Between two images showing the relative circumstances of each pixel, ICA (Principal Component Analysis) algorithm and procedures, There CDF trigonometric curve/3D graphs, Extract important parameters, It describes the application of load forecasting, For feature extraction, signal de-noising.
Platform: |
Size: 7168 |
Author:tgzpygi |
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Description: 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 analysis, Automatic identification in the matlab environment the size of the connected area, Is a good learning materials to learn PCA feature extraction, Using high-order cumulants of MPSK signal modulation recognition. Platform: |
Size: 6144 |
Author:unmkaqwuq |
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Description: 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 analysis, Automatic identification in the matlab environment the size of the connected area, Is a good learning materials to learn PCA feature extraction, Using high-order cumulants of MPSK signal modulation recognition. Platform: |
Size: 4096 |
Author:unmkaqwuq |
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Description: 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’number and length of equivalent table can be reduced. And the component’s features can also be
extracted during run length coding. Then using the way of scanning every pixel, the connected labels can be linked in a
single clock period. Finally the labels and features are merged in the procedure of equivalent table combination. The FPGA
simulation results indicate that, when connected component labeling and features extraction for a continuous binary image
are in progress, the processing time just includes the image input time and the equivalents table combination time. It is
more efficient than others and suitable for fast image recognition and tracking Platform: |
Size: 2195456 |
Author:杨松 |
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Description: Diabetic retinopathy is an important branch of ophthalmology. Non - proliferative diabetic retinopathy is used to detect Microaneurysms in the early stage. Microaneurysms are verified through fundus images; where in the fine red-dots near the blood vessels confirm this defect. Conventional methods and their weak resolution seldom can identify to such accuracies. In this work, we present a procedure to identify Microaneurysms with higher accuracy. The retinal vessels are extracted, from collected fundus image, using a Gabor wavelet which delivers high accuracy output. For accurate analysis the image it is sub divided into two regions, neighborhood and non-vessel neighborhood for expediting support vector machine (SVM) analysis. Further the SVM engine is trained for positive and negative samples of identified region fundus images. Then by sliding window technique, the entire test image is analyzed limiting analysis by SVM engine for near vessel region. This improves overall performance of the analysis and permits time available for a deeper/ sensitivity analysis of near vessel areas. The logic and the code has been tested on sample images and the results have been satisfactory. Platform: |
Size: 561690 |
Author:praneethtm@gmail.com |
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Search - extraction of connected component