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MRF_FCM
实现模糊聚类算法（FCM）与马尔科夫随机场空间约束（MRF）的图像分割、SAR图像变化检测(Fuzzy Cluster Method and Markov Random Field for Image Segmentation or SAR Change Detection)
- 2021-04-13 13:58:56下载
- 积分：1
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majoraxis
matlab计算二值图像几何特征中目标主轴长度(binary image matlab calculation of the geometric characteristics of the target length of major axis)
- 2009-03-06 17:05:42下载
- 积分：1
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MotionDetection
基于背景减除的单目标跟踪方法在Matlab中的实现，包含测试用avi文件(Background subtraction based on a single-target tracking method in Matlab, implementation, including test avi file)
- 2009-12-08 12:14:30下载
- 积分：1
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LCP1
说明： 左右圆偏振光的推导公式，与处理结果。传统意义上根据界面曲率的不同，对于确定的透镜，其正极性或负极性是确定了的。但根据等离子体天线方向角的改变，透镜的部分极性也随之改变，换言之，同一透镜中正负极性均存在且可控制。(Derivation formula and processing results of left and right circularly polarized light)
- 2020-03-23 15:57:27下载
- 积分：1
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CamShift2
Camshift和MOG结合的目标检测与跟踪算法(An alg of detecting object with camshift and mog)
- 2014-04-10 15:47:32下载
- 积分：1
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nsct_fusion
nsct——fusious图像融合系统代码nsct——fusious图像融合系统代码nsct——fusious图像融合系统代码(nsct- fusious image fusion system code nsct- fusious image fusion system code nsct- fusious image fusion system code)
- 2014-05-04 13:18:12下载
- 积分：1
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Learning-OpenCV
本书可作为信息处理、计算机、机器人、人工智能、遥感图像处理、认知神经科学等有关专业的高年级学生或研究生的教学用书，也可供相关领域的研究工作者参考。(This book can be used as information processing, computer, robot, remote sensing image processing, artificial intelligence, cognitive neuroscience and related professional senior student or graduate student s teaching resources, also can be used for reference in the related areas of research workers.)
- 2013-12-07 16:43:45下载
- 积分：1
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windowPCA
改进后的pca用于故障检测与诊断，应用于TE的化工过程，仿真结果比传统的pca方法效果更好(Pca for improved fault detection and diagnosis of chemical process applied to TE, the simulation results than the traditional method pca better)
- 2009-07-06 18:35:47下载
- 积分：1
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zishiyingyuzhi
自适应阈值分割代码，比较完整。C++builder编写，可运行/()
- 2020-12-05 18:19:24下载
- 积分：1
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LinearClassifier_1.0
基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)
- 2021-01-17 10:38:45下载
- 积分：1
