Description: 本程序采用GMM实现视频分割和简单的视频卡通化绘制。-GMM of the procedures used to achieve video segmentation and simple video cartoon drawing. Platform: |
Size: 7638016 |
Author:左丹 |
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Description: 用于进行视频分割的源码。采用GMM以及Meanshift算法目标进行，主要进行阴影跟踪。-Used for the source video segmentation. Meanshift algorithm using GMM, as well as targets, the main track for the shadow. Platform: |
Size: 5447680 |
Author:何仁 |
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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: hi this the paper on video segmentation using G-hi this is the paper on video segmentation using GMM Platform: |
Size: 288768 |
Author:kkrissh100 |
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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: 利用OPENCV來實現高斯混合模型的背景相減，可看到當前影像、前景及背景-OPENCV to achieve using GMM background subtraction, we can see the current image, foreground and background Platform: |
Size: 1024 |
Author:justin |
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Description: 一个利用GMM，SVGMM对图像进行分割的matlab程序，采用的是blobworld采集特征的方法。初始化也有备选多种方法-A use of the GMM, SVGMM image segmentation matlab program, the is blobworld collection features. Initialization also have options for a variety of methods Platform: |
Size: 888832 |
Author:刘帅帅 |
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Description: 图像处理ImageMatting的实现。主要思路是：
1）手工交互的给出一个前景区域的包围盒。
2）根据当前的前景和背景分割结果，分别估计前景和背景的GMM模型
3）用GraphCut算法进行分割
对上述2），3）两步进行迭代，得到比较好的分割结果
（分割-->估计前景背景-->分割）
4）matting-Image processing ImageMatting achieved. The main idea is:
1) gives a manual interaction foreground region bounding box.
2) According to the current foreground and background segmentation results were estimated foreground and background GMM model
3) segmentation algorithm with GraphCut
Above 2), 3) a two-step iterative obtain better segmentation results
(Split-> estimate foreground and background-> Split)
4) matting. Platform: |
Size: 2718720 |
Author:朱继祥 |
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Description: 自己写改进的经典算法，效果很好，可用于分割以及检测，精度高，效率高，可以直接执行，有简单的注释，可以作为算法的比较等，-Write your own improved classical algorithm works well and can be used segmentation and detection, high precision, high efficiency, can be executed directly, a simple note can be used as comparison algorithms, etc. Platform: |
Size: 3208192 |
Author:汪溁鹤 |
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Description: this program treat image processing and segmentation methods and can help for functions which concern segmentation, hidden markov segmentation Platform: |
Size: 3209216 |
Author:gherbi |
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Description: 针对摄像机固定下的复杂背景环境，对采集到的视频图像的图像数据用混合高斯背景建模方法实现前景/背景分割，实现运动目标检测和跟踪。在进行前景检测前，先对背景进行训练，对图像中每个背景采用一个混合高斯模型进行模拟，每个背景的混合高斯的个数可以自适应。然后在测试阶段，对新来的像素进行GMM匹配，如果该像素值能够匹配其中一个高斯，则认为是背景，否则认为是前景。由于整个过程GMM模型在不断更新学习中，所以对动态背景有一定的鲁棒性。最后通过对一个有树枝摇摆的动态背景进行前景检测，取得了较好的效果。-For complex background environment under fixed camera, the image data captured video images using Gaussian mixture background modeling method implementation foreground/background segmentation, detecting and tracking moving objects. Before foreground detection, the first training background, the background of each image using a Gaussian mixture model to simulate the number of each Gaussian mixture background adaptively. Then in the testing phase, the pixels on the new GMM match, if the pixel value is able to match one of the Gaussian, then that is the background, otherwise considered a prospect. Since the whole process is continuously updated GMM model in the study, so the dynamic context of a certain robustness. Finally, on a tree swing dynamic background foreground detection, achieved good results. Platform: |
Size: 5177344 |
Author:axin |
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Description: 本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.) Platform: |
Size: 1024 |
Author:xiaoxiaofish |
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