Description: 使用高斯模型期望值最大化演算法,做圖形分割
Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture
-Gaussian model using expectation maximization algorithm, to do graphics segmentation Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture Platform: |
Size: 1024 |
Author:李致賢 |
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Description: 改进的高斯混合模型用于运动目标的检测和分割,利用C++和matlab混合编程.-Improved Gaussian mixture model for moving object detection and segmentation, the use of C++ and matlab programming mixed. Platform: |
Size: 271360 |
Author:杨佳 |
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Description: A Spatially-Constrained Mixture Model for Image Segmentation, by
K. Blekas, A. Likas, N. Galatsanos and I. Lagaris-A Spatially-Constrained Mixture Model for Image Segmentation, by
K. Blekas, A. Likas, N. Galatsanos and I. Lagaris Platform: |
Size: 29696 |
Author:cobble |
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Description: OPENCV下基于高斯混合模型的图像分割，程序中还有
基于大津法的图像分割和金子塔分割。-OPENCV Based on Gaussian mixture model of image segmentation, the program also includes Otsu method based on image segmentation and the segmentation pyramid. Platform: |
Size: 497664 |
Author:jiaojiao003 |
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Description: A common method for real-time segmentation of
moving regions in image sequences involves “background
subtraction,” or thresholding the error between
an estimate of the image without moving objects and
the current image. The numerous approaches to this
problem differ in the type of background model used
and the procedure used to update the model. This paper
discusses modeling each pixel as a mixture of Gaussians
and using an on-line approximation to update
the model. The Gaussian distributions of the adaptive
mixture model are then evaluated to determine which
are most likelyt o result from a background process.
Each pixel is classified based on whether the Gaussian
distribution which represents it most effectivelyis considered
part of the background model. Platform: |
Size: 186368 |
Author:ajinkya |
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Description: 一种基于肤色分割、区域分析和模板分布的彩色图像人脸检测算法.首先对输入的彩色图像利用混合高斯模型和亮度模型进行分割,然后根据人脸五官的结构特征对得到的区域进一步分析处理,获得所有可能的候选人脸.接着构造了一种基于双眼和人脸模板的概率模型并利用其对候选人脸进行最终检测.-Based on skin color segmentation, regional analysis and the template in color images of face detection algorithm. First, the input color image using mixture Gaussian model and the brightness model segmentation, then under the facial features of the structure on further analysis by region treatment to obtain all possible candidates face. then constructs a template based on the eyes and face the probability model and use its candidates face final test. Platform: |
Size: 586752 |
Author:麦高 |
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Description: learning algorithm for finite mixture model and test
its application into motion segmentation-learning algorithm for finite mixture model and test
its application into motion segmentation Platform: |
Size: 2259968 |
Author:loossii |
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Description: This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification. Platform: |
Size: 653312 |
Author:kvga |
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Description: 基于变权重MRF的图像分割算法，特征场是使用混合高斯模型，标记场使用Pott模型，基于迭代条件模式进行分割-MRF based on weighted image segmentation algorithm, feature field is the use of Gaussian mixture model, using the tag field Pott model segmentation based on iterative model conditions Platform: |
Size: 84992 |
Author:马志远 |
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Description: 混合高斯模型，直接应用于图像分割，简单易行，傻瓜式操作，保准让你爱不释手。-gaussian mixture model applied to image segmentation, easy, convenient, you are sure to love it. Platform: |
Size: 9216 |
Author:黄桃 |
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Description: We derive an asymptotic Newton algorithm for Quasi-Maximum
Likelihood estimation of the ICA mixture model, using the ordinary
gradient and Hessian. The probabilistic mixture framework yields an
algorithm that can accommodate non-stationary environments and
arbitrary source densities. We prove asymptotic stability when the
sources models mixture match the true sources. An example application
to EEG segmentation is given Platform: |
Size: 494592 |
Author:msreddy |
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Description: 位图图像稳定区域分割的种子点选取条件，区域定义 区域分割 高斯混合模型 高斯分布 种子点选取 阈值选取 灰度范围-Bitmap image stabilization region segmentation of the seed point selection conditions, regional definition Region segmentation Gaussian mixture model Gaussian distribution The seed point selection Threshold selection Gray scale range Platform: |
Size: 1105920 |
Author:黄哲 |
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Description: This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
-This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
Platform: |
Size: 974848 |
Author:Deepesh |
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Search - mixture model segmentation