搜索资源列表
BNC
- 朴素贝叶斯分类器(Naive Bayes Classifier),可以分类任意属性个数和目标状态的离散样例。-Naive Bayesian classifier (Naive Bayes Classifier), can be categorized arbitrary number of attributes and objectives of the state of the discrete sample.
KernelDiscriminantAnalysis(manyclassiers)
- 用于目标识别的核辨别分析程序,含有大量的分类器子程序。-For target identification of nuclear analytical procedures to identify, contain many classifier subroutine.
NearestFeatureLineClassifier
- 用于空中目标识别的最近特征线分类器子程序。-Aerial targets for the identification of Nearest Feature Line Classifier subroutine.
NearestFeaturePlaneClassifier
- 用于空中目标识别的最近特征平面分类器子程序。-Aerial targets for the identification of the recent characteristics of plane classifier subroutine.
Moving_Target_Classification_and_Tracking_from_Rea
- 这是一篇有关运动目标分类的英文文献。这篇文献以离散度作为分类特征,利用时间一致性,提高了分类性能。内容也不是很深奥,相信对初学者有较大的帮助。-This is an article on the classification of moving targets in English literature. This dispersion in the literature as the classification features, use of time consistency, impro
Tracking_and_classifying_moving_objects_from_video
- Q. Zhou, J.K. Aggarwal. Tracking and Classifying Moving Objects from Video. 这篇文章另辟蹊径,利用“紧凑度值的变化、运动方向的变化”,区分人、人群、机动车。达到良好的分类效果。是运动目标分类领域的好文章。-Q. Zhou, JK Aggarwal. Tracking and Classifying Moving Objects from Video. This article open a new path, by us
Object_Classification_and_Tracking_in_Video_Survei
- Zang, Q. and Klette, R. Object Classification and Tracking in Video Surveillance. 这篇文章是有关多运动目标分类的文章。使用常用的长宽比作为分类特征,结合角点特征。提高了人车的分类效果。-Zang, Q. and Klette, R. Object Classification and Tracking in Video Surveillance. This article is about the many obj
873125video_demystified
- 智能视频分析,目标检测,物体分类,需要来下载-video analysis
ahpzaizhengdixitong
- 对于阵地的生存概率计算, 可以使用文献1 中的解析公式作粗略估算, 具体方法为, 把整个阵地看作一 个点目标, 即以阵地的中心作为攻击点。求解出某型弹对阵地的破坏半径, 代入解析式即可。但在实际情况 下, 相对于来袭武器的破坏半径, 把阵地看作点目标必然引起很大的误差。所以, 本文拟用层次分析法对阵地 这个系统进行可能受到攻击的目标分类, 按各分项指标的重要性得出系统的生存概率-Probability of survival for the position calculation,
03
- 真实场景下视频运动目标自动提取方法.主要的研究内容包括运动物体检测,分类和跟踪,研究成果可以广泛地应用在交通管理系统,视频监视系统和军事目标跟踪系统,同时还可以应用在基于内容的视频数据压缩编码中。-Real video scenes under the automatic extraction method of moving targets. The main content includes moving object detection, classification and tracki
e
- 基于边缘特征的水下目标分类识别算法 摘要:基于形状相似度的概念,利用目标边缘轮廓特征,给出一种基于距离多集方法的水雷目标性状分类算法。-Based on the edge of the characteristics of the underwater target classification and recognition algorithm Abstract: Based on the concept of shape similarity, using the edge of the
HaarTraining
- OPENCV训练过程的说明文档, 在样本创建;训练分类器;利用训练器进行目标检测作了操作说明 源程序在安装OPENCV时,自带apps\HaarTraining-OPENCV training process documentation, created in the sample training classifier the use of training devices for target detection was made in the installation inst
Recognition
- 運動識別 在摄像机监视的场景范围内,对出现的运动目标进行检测、分类及轨迹追踪,可应用于各种监控目的,如周界警戒及入侵检测、绊线检测、非法停车车辆检测等。-Movement Recognition ' scene in the scope of surveillance cameras, the emergence of the moving target detection, classification and tracking, monitoring can be applied
xiangsidu
- 遥感图像的多光谱目标识别,相似度方法实现分类-The multi-spectral remote sensing image object recognition, similarity classification method
IJCV01-Oliva-Torralba
- 著名的GIST描述子,广泛用于目标识别,分类等-Well-known GIST descr iptors are widely used in target identification, classification, etc.
20080111
- 有关图像的目标识别:"给出一种基于特征分类辨识的合成孔径雷达图像目标检测方法#用恒虚警和扩展分形方法对3&E图像进行目 标检测后用面积和峰值能量比算子辨识目标和背景杂波!去除一部分虚警!用小波域主成分分析对每个检测窗口内的图 像提取特征向量!用支持向量机对提取得到的特征向量进行分类!辨识目标和背景杂波!完成目标检测#使用&K?3数 据对该方法进行验证和分析!实验结果表明!经过特征分类辨识后!在检测率不变的情况下!虚警数目显著降低# -Related to the image ta
MILL
- 模式识别中,多标签标记中的经典代码,主要用于场景分类,目标识别,结合svm和boost算法对自然场景进行分类,真的很不错,看看吧-Pattern Recognition, multi-tagged in the classic code, mainly used for scene classification, object recognition, combined with svm and boost the natural scene classification algorithm,
Floatboost
- 在基于特征提取方法之上, 研究用算法对目标多视角问题进行分类器设计。在对 图像进行独立成分分析后, 针对多姿态角目标识别问题, 提出了角度优先粗分类的设计方法-Feature extraction method based on above research objective of the algorithm to classify multi-view design problem. In the right image independent component analysis, t
Data_fusion_Fisher_theroy
- 针对多个特征指标的多传感器数据融合问题,将Fisher理论和多数投票法相结合进行数据融合来增加识别率。该方法首先通过Fisher理论得到多个判别函数,然后通过多数投票法继续对得到的判别进行分类得到最后的识别决策。该方法适合多个特征目标识别,计算简单。易于实现-Indicators for multiple features multi-sensor data fusion problem, Fisher majority vote of the Combination of theory and
MATLAB
- 优化问题分类:(非)线性规划、整数规划、0-1 规划、(多)目标规划、(与时间有关的)动态规划、(系数是随机变量的)随机规划。 -Optimization problem categories: (non-) linear programming, integer programming ,0-1 plan, (multiple) goal programming, (and time-related) dynamic programming, (coefficient is a random