基于数字图像处理的油菜种子形状特征参数提取及模糊聚类分析

基于数字图像处理与分析技术，利用采集的油菜种子图像提取7个种子形状特征参数，选用圆形度和短长轴比作为油菜种子分类参数，应用模糊C-均值聚类分析方法把全体样本分为3类并得到油菜种子形状量化参数，在此基础上对样本油菜种子形状进行量的规定．进一步讨论基于种子形状的油菜种子分级，为油菜种子分类与鉴定提供可靠依据．     

基于AAM关键特征点提取的人脸照片分类方法的研究与应用

主要研究基于人脸局部形状特征分类的方法,首先利用AAM的人脸形状特征点定位算法,提取出有用的人脸特征点,构成人脸下颚形状、眉毛、眼睛、鼻子和嘴巴的局部形状特征;然后采用基于形态面指数和下颚宽指数的ISODATA方法进行自动聚类,实现了百万量级人脸照片库的自动分类,有助于进一步提高人脸识别查询的速度和精度。     

基于数字图像技术的卵石模糊聚类

为了实现卵石分类数字化,利用数字图像技术获取卵石图像边缘信息,基于最小二乘圆度误差概念构建出一种新的描述卵石形状的参数系统。应用模糊数学原理,使用最大树法对从野外采集的卵石样本进行了模糊聚类识别实验;选取不同的聚类模糊度,结果对应不同的聚类细度、不同的模糊分类准度。对其中一种聚类结果,将实物图与几种标准图进行数据对比确定了图形分类特征,并使用多维平面图法进行聚类结果分析,进一步证明了直观判断卵石形状特征参数与模糊聚类关系的可行性。实验表明,该卵石分类法具有一定的实用性和先进性。     

基于特征词及形状模型的图像类别学习

一类图像的特征及其分布在很大程度上表达了该类的主要信息.根据这一思想,结合图像中的像素信息及形状信息提出一种类图像识别方法.对于一类给定的样本图像,首先提取每一幅图像的显著特征,根据特征分布提取特征区域;然后对所有的特征区域进行聚类得到特征词典,基于特征词及形状信息建模,同时采用最大似然估计的方法进行学习得到模型参数;最后结合特征词模型及形状模型对测试图像进行识别.实验结果表明,该方法能够有效地对2类图像进行分类和识别,同时对多数类图像也能进行较为准确的分类和识别.     

基于ε-KLD的文本分类方法

基于Kullback-Leibler Distance(KLD)的文本分类作为一种新的分类方法在对大规模文本和高维特征向量进行分类时表现出较高的分类精度,超出了基于相似度量的TFIDF方法。对KLD文本分类方法进行研究,利用信息增益方法进行特征提取,将预定义参数ε引入KLD公式得到基于ε-KLD的文本分类方法。结果表明该方法简化了类和文档的特征向量的计算,并取得了和KLD相当的分类精度,其总体性能超过了KLD方法。     

基于协同进化算法的高维模糊分类系统的设计

基于协同进化算法，提出一种高维模糊分类系统的设计方法．首先定义系统的精确性指标，给出解释性的必要条件，利用聚类算法辨识初始模型．相互协作的3类种群分别代表系统的特征变量、规则前件和模型隶属函数的参数，适应度函数采用3类种群合作计算的策略，在算法运行中利用基于相似性的模型简化技术约简模糊系统，最后利用该方法对Wine问题进行研究．仿真结果表明该方法能够对高维分类问题的特征变量进行选择，同时利用较少规则和模糊集合数达到较高的识别率．     

基于模式运动的一类生产过程调节性能与聚类参数关系

为研究基于模式运动的系统动力学描述方法中聚类参数对生产过程调节性能的影响,给出描述系统动态调节性能与产品质量调节性能的指标,分析并建立了聚类参数与系统调节性能间的关系;介绍了基于模式运动的一类复杂生产过程建模方法,并利用LMI方法给出了状态反馈控制器设计方法;提出了基于粒子群优化方法的最大熵聚类算法,定义并提取了系统调节性能指标;利用提出的新的覆盖分类神经网络,建立最大熵聚类方法的参数与调节性能间的映射关系,并分析了分类网络泛化能力;采用实际烧结矿生产数据进行仿真,结果表明所提方法可以分析与建立调节性能与聚类参数间的关系,且可为实际生产中聚类参数的选择提供一定的依据.     

面部表情图像的分析与识别

本文通过对若干类面部表情图像的分析，建立了基于部件分解组合的人脸图像模型。根据对部件形状和相对位置的分析，提出了表情的分类树，建立了表情模型的向量表示。根据能量优化原理，利用模板匹配方法提取目标特征，得到人脸表情的表征向量，由模式分类方法实现表情的识别。     

带形状参数曲线的最优参数取值问题研究

带形状参数的曲线中所带形状参数的取值通常是一个区间,但在实际问题中往往 需要确定形状参数的最优取值,以使得曲线具有良好的光顺性。针对这一问题,首先基于曲线 的光顺准则建立了一个求解带形状参数曲线最优参数取值的通用数学模型,然后给出了利用遗传 算法求解该模型的具体步骤,最后以两类带形状参数的曲线为例验证了所提出方法的有效性。     

保健酒中可见异物的神经网络复合分类方法

保健酒中可见异物个体微小、形状复杂多变,不利于自动分拣。为此,提出一种基于异物几何特征和不变矩特征的神经网络复合分类方法。通过单层感知器进行一级分类以检测毛发类异物,利用BP网络对非毛发类异物进行二级分类。为提高BP网络训练速度,设计动量因子和学习速率可自适应调整的改进学习算法。实验结果表明,该分类方法识别准确度高,识别速度快。     

标准正则化理论的一些推广和应用

在标准正则化理论中建议一类局部相互作用场和自适应正则项。局部相互作用场包括通常的误差项和一窗口函数。窗口的大小，取向和形状可以视具体的应用加以调整。自适应正则项用来处理不连续变化。这两项导致一对称的能量泛函。最简单的梯度下降算法通常在参数变化和有噪声时也很稳定，有效。     

基于V-矩的图像分类算法

基于一类不仅含有连续函数,还含有间断函数的正交完备函数系——V-系统,提 出相应的V-矩函数,并将之应用到图像分类中。V-系统中基函数的间断特性,使得V-矩函数 在描述含有多个闭合边界的形状时有特别的优势,这种优势表现为对这类复杂形状的特征提取 更加准确。因此用V-矩可以得到一种图像分类的有效算法。在几个通用数据库中的图像分类 实验表明,本文算法较Zernike 矩、不变矩和几何中心矩有更高的准确率,对噪声不敏感,特 别在含有多个闭合边界的复杂形状分类问题中,本文方法优势更为显著。     

Determining membership functions and minimum fuzzy support in finding fuzzy association rules for classification problems

This paper propose a new method, that employs the genetic algorithm, to find fuzzy association rules for classification problems based on an effective method for discovering the fuzzy association rules, namely the fuzzy grids based rules mining algorithm (FGBRMA). It is considered that some important parameters, including the number and shapes of membership functions in each quantitative attribute and the minimum fuzzy support, are not easily user-specified. Thus, the above-mentioned parameters are automatically determined by a binary string or chromosome is composed of two substrings: one for each quantitative attribute by the coding method proposed by Ishibuchi and Murata, and the other for the minimum fuzzy support. In each generation, the fitness value, which maximizes the classification accuracy rate and minimizes the number of fuzzy rules, of each chromosome can be obtained. When reaching the termination condition, a chromosome with maximum fitness value is then used to test its performance. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that proposed method performs well in comparison with other classification methods.     

Using geometric constraints through parallelepipeds for calibration and 3D modeling

This paper concerns the incorporation of geometric information in camera calibration and 3D modeling. Using geometric constraints enables more stable results and allows us to perform tasks with fewer images. Our approach is motivated and developed within a framework of semi-automatic 3D modeling, where the user defines geometric primitives and constraints between them. In this paper, first a duality that exists between the shape parameters of a parallelepiped and the intrinsic parameters of a camera is described. Then, a factorization-based algorithm exploiting this relation is developed. Using images of parallelepipeds, it allows us to simultaneously calibrate cameras, recover shapes of parallelepipeds, and estimate the relative pose of all entities. Besides geometric constraints expressed via parallelepipeds, our approach simultaneously takes into account the usual self-calibration constraints on cameras. The proposed algorithm is completed by a study of the singular cases of the calibration method. A complete method for the reconstruction of scene primitives that are not modeled by parallelepipeds is also briefly described. The proposed methods are validated by various experiments with real and simulated data, for single-view as well as multiview cases.     

Knitted fabric defect classification for uncertain labels based on Dempster–Shafer theory of evidence

A new approach for classification of circular knitted fabric defect is proposed which is based on accepting uncertainty in labels of the learning data. In the basic classification methodologies it is assumed that correct labels are assigned to samples and these approaches concentrate on the strength of categorization. However, there are some classification problems in which a considerable amount of uncertainty exists in the labels of samples. The core of innovation in this research has been usage of the uncertain information of labeling and their combination with the Dempster–Shafer theory of evidence. The experimental results show the robustness of the proposed method in comparison with usual classification techniques of supervised learning where the certain labels are assigned to training data.     

Spatial classification using fuzzy membership models

In the usual statistical approach to spatial classification, it is assumed that each pixel belongs to precisely one of a small number of known groups. This framework is extended to include mixed-pixel data; then, only a proportion of each pixel belongs to each group. Two models based on multivariate Gaussian random fields are proposed to model this fuzzy membership process. The problems of predicting the group membership and estimating the parameters are discussed. Some simulations are presented to study the properties of this approach, and an example is given using Landsat remote-sensing data     

Detection of linear and circular shapes in image analysis

A two-step algorithm is proposed for estimating linear and circular shapes in noisy images. Initially and based on a previously proposed method, the pixels which are close to the edges of the shape are detected. These edges are assumed to be coming from a mixture of (linear or circular) regression functions and the parameters of these functions are estimated. An example with a triangle demonstrates the immense advantage of using an outlier robust estimator for the edge points. A second example deals with a problem from biology where the detection of circular shapes of fungi colonies is of interest.     

Hyperspectral image classification based on morphological profiles and decision fusion

Extended morphological profile (EMP) is an important mathematical tool for extracting structural information from the hyperspectral images. However, the accuracy of the EMP-based classification is greatly influenced by the choice of structuring element (SE). In this article, two supervised classification frameworks multiclassifier system with morphological profiles (MCSMP) and MCSMP2 are proposed that exploit rich spectral and structural information of hyperspectral images using EMPs and multiclassifier system for better classification than conventional methods. The EMPs with SEs of multiple shapes are used instead of one particular shape to better detect the response from the structures in the image. The EMPs created from SEs of different shapes are independently classified followed by decision fusion to generate final classification map. The classification results are compared with the conventional pixelwise and other EMP-based methods. The experimental results from three different types of hyperspectral data sets demonstrate that the proposed methods have significantly improved the spectral approach and outperformed the other studied methods in terms of classification accuracy. The new methods are more robust to the noise and produce good classification accuracy with very limited training samples. Various decision fusion techniques are evaluated, which performed differently in tested scenarios. Two different classifiers, Support Vector Machine (SVM) and random forest, are used in the experiments. It is shown that the proposed methods perform better with random forest classifier.     

2-D shape classification using hidden Markov model

The authors present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters. This approach segments closed shapes to make classifications at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. An orientation scheme is described to make the overall classification insensitive to shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added