Online face recognition system based on multi-view algorithm and Kernel PCA

An online face recognition system is presented in the paper. To online face recognition system, we should consider the recognition rate, the image compression and image size. In the paper we researched the innovation technologies for face recognition system, including Kernel Principal Component Analysis （Kernel PCA）, Delta low-pass wavelet filter, and face recognition algorithm using multiple images. Kernel PCA is derived to classify the characteristics of training images in the database. Delta low-pass wavelet filter is used to reduce the image size. A face recognition algorithm using multiple images is presented to improve the recognition rate. Simulation experiment shows that in the case of packet loss recognition rate is improved highly.     

On spam detection based on cognitive pattern recognition

A method of spam detection, based on cognitive pattern recognition, had been proposed. The connection between Email category and cognition of Email user interest within life and work, had been analyzed. Under the guidance of cognitive pattern recognition theory, the mechanism of spam detection, based on intelligent cognition of Email user interest within life and work, had been discussed. Then the spam detection algorithm and its concrete implementation was given. Experimental results demonstrate that the spare detection algorithm has a good learning ability, scalability, and a good ability to achieve high recognition accuracyn     

一种货币识别传感器有效性与精度分析方法

货币识别技术利用各种传感器来检测和测量货币的各类信息,选择有效、精确的传感器是实现货币识别的第一步工作;在真币性质比较稳定,以及货币样本容易获取的现实条件下,讨论了真币性质的特性,提出了基于正态分布和向量范数的检测方法,它通过对货币样本数据的统计分析来判断传感器的有效性和精确性;此方法简洁实用,为传感器的选择提供了客观依据。     

基于神经网络数字识别方法的研究

针对无指针式仪表表盘的数字识别问题,提出一种基于特征提取和粗糙集特征约简的神经网络数字识别方法;该方法首先利用数字图像预处理技术处理图像并利用特征提取方法提取数字图像特征,然后利用粗糙集理论进行特征约简,最后将约简后的信息输入到训练好的神经网络进行识别;实验表明,相对于传统方法,该方法具有识别率高、速度快的特点,具有较高的实用价值;并且该方法在保留神经网络高鲁棒性的同时,为快速准确地进行数字识别开辟了新的途径。     

On the Sequential Accumulation of Evidence

In this paper, we introduce a method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. We develop a probabilistic framework, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints using Bayesian chaining until a definitive assertion can be made. To illustrate the theory we show how the characteristics of belief distributions can be exploited in a model-based recognition problem, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. We illustrate the robustness of the algorithm through recognition experiments in two very different contexts: (1) a highly structured recognition context where 3-D parametric models can be estimated directly from range data, (2) a complex environment, where the relationship between the data and the model is learned through an appearance-based strategy. Specifically, the flow fields computed through the object's motion are used as structural signatures for recognition.     

Shape Morphing-Based Control of Robotic Visual Servoing

We present an approach for controlling robotic interactions with objects, using synthetic images generated by morphing shapes. In particular, we attempt the problem of positioning an eye-in-hand robotic system with respect to objects in the workspace for grasping and manipulation. In our formulation, the grasp position (and consequently the approach trajectory of the manipulator), varies with each object. The proposed solution to the problem consists of two parts. First, based on a model-based object recognition framework, images of the objects taken at the desired grasp pose are stored in a database. The recognition and identification of the grasp position for an unknown input object (selected from the family of recognizable objects) occurs by morphing its contour to the templates in the database and using the virtual energy spent during the morph as a dissimilarity measure. In the second step, the images synthesized during the morph are used to guide the eye-in-hand system and execute the grasp. The proposed method requires minimal calibration of the system. Furthermore, it conjoins techniques from shape recognition, computer graphics, and vision-based robot control in a unified engineering amework. Potential applications range from recognition and positioning with respect to partially-occluded or deformable objects to planning robotic grasping based on human demonstration.     

A model-based hand gesture recognition system

This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000     

模式特征滤波

借鉴了传统的信号频谱滤波原理,根据最小均方误差原则,在特征空间法模式识别中提出了“特征空间维纳滤波”算法,它充分利用先验知识,为模式识别系统构造一个线性滤波器;理论和实验结果表明,维纳滤波使总偏差达到最小,实验结果还表明它对识别性能有一定改善。     

基于联合时频分析的混合神经系统在信号分类与模式识别中的应用

介绍了一种基于STFT,WVD和Wavelet变换的混合神经系统在特征提取和信号分类中的应用.它运用时频信号处理技术与混合神经网络结构以及高级训练算法相结合的设计思想,辅之以主元分析和全局决策融合策略,对传统的模式识别技术进行了有效地改进.本文将所述基于WT,STFT和WVD的混合神经系统信号分类器(WSWHNS)的算法程序嵌入一汽车实时智能故障诊断软件包中做了现场实验,获得了非常满意的诊断效果.