Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code. Platform: |
Size: 50271 |
Author:pbt |
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Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code. Platform: |
Size: 50176 |
Author:pbt |
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Description: K均值聚类算法 由于对纹理图像使用灰度共生矩阵分割效果不明显 因此该算法使用图像频域进行处理-K-means clustering algorithm because of the texture image segmentation using the gray co-occurrence matrix effect was not obvious, therefore use the algorithm for processing images in frequency domain Platform: |
Size: 304128 |
Author:小五子 |
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Description: In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
-In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
Platform: |
Size: 2048 |
Author:pramod |
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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 - segmentation of images using k means algorithm