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ISODATA算法是一种基于统计模式识别的，非常经典的非监督学习动态聚类算法，有较强的实用性。ISODATA算法不仅可以通过调整样本所属类别完成样本的聚类分析，而且可以自动地进行类别的“合并”和“分裂”，从而得到类数比较合理的聚类结果。-ISODATA algorithm is based on statistical pattern recognition, unsupervised learning is the classic dynamic clustering algorithm
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This code is an MPI version of the sorting algorithm QuickSort in c++. The partitioning step is meda sequantially, but the split and merge steps are made using Send and Recieve methods.
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Split and merge Algorithm for Image Processing . It uses a quad tree as data structure, which is created at running time using breath first method, and a stopping criterion . The stoping criterion method can be changed , i just used to the difference
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Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds-Free Split and Merge Expectation-Maximization algorithm for Multivariate
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SPLITMERGE Segment an image using a split-and-merge algorithm.
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一种改进的图象分割程序,分割出来效果相当好,尤其对于丰富的图象,保证别人没有上传过-Based on graph cut image segmentation algorithm, split the result was very good, especially for texture-rich images, ensure that no one else uploaded
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SPLITMERGE Segment an image using a split-and-merge algorithm.
G = SPLITMERGE(F, MINDIM, @PREDICATE) segments image F by using a
split-and-merge approach based on quadtree decomposition. MINDIM
(a positive integer power of 2) specifies t
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This is a split and merge type of segmentation algorithm
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SPLITMERGE Segment an image using a split-and-merge algorithm.
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基于区域的分裂与合并的算法 能够对图像进行简单的分裂与合并-The image can be split and merge based on a simple split and merge algorithm region
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区域生长与合并的算法。能够简单地对图像进行区域分裂与合并-Region growing and merging algorithm. The image can be easily split and merge area
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segments image F by using a
split-and-merge approach based on quadtree decomposition. MINDIM
(a positive integer power of 2) specifies the minimum dimension
of the quadtree regions (subimages) allowed. If necessary, the
program pads the in
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This paper advances prior work by proposing a joint learning
framework to simultaneously identify the spatial and temporal
extents of the action of interest in training videos. To get pixel-level
localization results, our method uses dense traj
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