A combined nonparametric approach to feature selection and binary decision tree design

An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classification decision at each node. By combining the feature selection with the design of the classifier, only the most informative features are retained for classification.     

Feature selection by interactive clustering

The results of experiments with a novel criterion for absolute non-parametric feature selection are reported. The basic idea of the new technique involves the use of computer graphics and the human pattern recognition ability to interactively choose a number of features, this number not being necessarily determined in advance, from a larger set of measurements. The triangulation method, recently proposed in the cluster analysis literature for mapping points from l-space to 2-space, is used to yield a simple and efficient algorithm for feature selection by interactive clustering. It is shown that a subset of features can thus be chosen which allows a significant reduction in storage and time while still keeping the probability of error in classification within reasonable bounds.     

Leukocyte image analysis in the diff3 system

The diff3 System uses image processing and pattern recognition techniques to automatically analyze normal and abnormal white blood cells in a blood smear. The system consists of a spinner which creates a monolayer of cells on a glass slide, a stainer utilizing Wright's stain, the reagents to support the spinner and stainer, and an analyzer for automatic slide handling, analysis and report generation. The analyzer incorporates a wide range of image processing functions, including the generation and storage of gray scale image data, whole-field and partial-field image histogramming, and high-order binary image texture analysis and image transformation using the Golay processor (GLOPR). This paper describes the manner in which these hardware capabilities are used for white cell acquisition, scene segmentation and feature analysis. It concludes with some examples of texture extraction which illustrate the power of the Golay processor as a tool for image analysis.     

YX3高效三相异步电动机的研制

依据用户要求和行业发展指导意见,从电磁设计、工艺创新和结构改进等方面较全面地阐述了YX3高效率三相异步电动机的设计.样机通过了国家防爆电气产品质量监督检测中心的性能检测,取得了中国节能认证证书,通过了中国能效中心能效等级2级的备案,获得了河南省科技成果奖.     

条格织物组织识别中的图像分割方法

针对条格组织的特点，运用K-均值算法对从织物图像中提取的纹理谱统计特征进行分类，提取出条格组织中不同织物组织结构块，从而将这类组织的织物图像分割成一系列简单图像，使织物组织的识别过程大大简化。     

模块二维主成分分析——人脸识别新方法

提出了模块二维主成分分析(M2DPCA)线性鉴别分析方法。M2DPCA方法先对图像矩阵进行分块，对分块得到的子图像矩阵直接进行鉴别分析。其特点是：能有效地降低模式原始特征的维数；可以完全避免使用矩阵的奇异值分解，特征抽取方便；此外，2DPCA是M2DPCA的特例。在ORL人脸库上试验结果表明，M2DPCA方法在识别性能上优于PCA，比2DPCA更具有鲁棒性。     

CHRIS 高光谱图像森林类型分类方法比较研究

以长白山为试验区,选择CHRIS/PROBA高光谱零度角遥感数据,在对其进行预处理的基础上,通过应用最大似然法(MLC)、最小距离法、支持向量机法(SVM)和光谱角填图法(SAM)等几种常用的高光谱遥感分类方法对影像进行森林类型分类。利用混淆矩阵对分类结果进行验证,结果显示:在高光谱遥感森林类型分类中,SVM总体分类精度最高,为84.60％;其次是MLC,为 83.53％,最小距离法73.81％,SAM 56.49％。Kappa系数从高到底为:SVM 0.78,MLC 0.77,最小距离法0.68,SAM 0.52。经过比较分析,得出SVM分类方法精度最高,这表明该方法用于高光谱遥感森林分类中的实用性和优越性。     

基于PHOG特征的行人检测算法研究

HOG特征对行人轮廓有很好的描述能力,但基于HOG特征的行人检测存在检测速度慢、漏检率较高的问题,使得该算法的实践应用范围受限。本文针对检测速度慢、漏检率较高的问题,提出了一种基于PHOG特征的行人检测算法。首先,提出了PHOG特征,该特征对cell内的梯度特征进行强化,增大了目标与背景的梯度分布区别,从而使目标更容易被分类器学习和识别。然后提出了构建特征金字塔的方法,并对PHOG特征进行有效地降维,大幅度减少了检测时间。试验结果表明,本文提出的PHOG-PCA特征将漏检率从35%降到了22%,检测速度也比一些流行算法快。     

视觉分类识别系统设计

针对企业生产过程中零件混装问题,设计了使用基于高斯混合模型的背景分离算法,实现稳定且灵活的背景分离效果,使用基于等级灰度、形状相似和简单轮廓的特征提取算法,实现有效且稳定的特征数据提取效果,利用基于xml数据存储的多层神经网络算法,实现物品种类动态变更的效果.通过对图像进行分离、提取、识别后,达到分类的效果,结果表明,...