Robust Segmentation Method for Doorplate Recognition System

Robot navigation based on character recognition is an effective vision method for com- pensating the disadvantage of ultrasonic and infrared sensors.A typical example of character recog- nition for mobile robot navigation is the doorplate recognition system.The captured doorplate images contain unexpected noise from irregular illumination conditions,various imaging angles,dif- ferent imaging distances,etc.The unexpected noise may still exist after segmentation step.In this paper,a robust segmentation method based on speculating the candidates of the characters and feeding back the classification result to the segmentation process is presented.If the candidates of doorplate characters cannot be determined at the segmentation step,a speculation according to known knowledge is executed.The threshold for character extraction from candidates is adjusted when the corresponding character is rejected after classification.The experimental results indicate that the recognition results are effectively improved with the proposed segmentation method.     

门牌识别系统中的鲁棒性分割方法

Robot navigation based on character recognition is an effective vision method for compensating the disadvantage of ultrasonic and infrared sensors. A typical example of character recognition for mobile robot navigation is the doorplate recognition system. The captured doorplate images contain unexpected noise from irregular illumination conditions, various imaging angles, different imaging distances, etc. The unexpected noise may still exist after segmentation step. In this paper, a robust segmentation method based on speculating the candidates of the characters and feeding back the classification result to the segmentation process is presented. If the candidates of doorplate characters cannot be determined at the segmentation step, a speculation according to known knowledge is executed. The threshold for character extraction from candidates is adjusted when the corresponding character is rejected after classification. The experimental results indicate that the recognition results are effectively improved with the proposed segmentation method.     

一种基于轨迹分布模式的网络机器人用户意图识别方法

在Internet移动机器人遥操作系统中,网络本身的不确定性是影响系统性能和用户操作效果的一个重要因素。提出了一种新的基于轨迹分布模式的方法,能对用户意图进行建模和识别,利用网络机器人的本身的自主性削弱网络不确定性给系统带来的不良影响。通过实验验证,发现这种方法能够较好地识别出用户的控制意图,并根据识别结果自主执行用户期望完成的任务,从而改善了用户的操作体验和机器人接受用户操作的表现,提高了遥操作系统的效率。     

神经网络方法用于分辨三种化学物质

采用了一种基于神经网络的杂交算法，用于识别来自氧化锡传感器阵列的三种化学品：丙酮、甲醇和乙醇的信号．算法利用梯度收敛方法并结合仿真退火和遗传算法进行权值初值化来加速运算速度和提高分类准确度。这种方法能较好地避免局部最小点，大量运算实例用来构造最优网络结构并说明这种方法是较好的模式分类方法并可以作为“人工电子鼻”的数据处理方法。     

A Pattern Recognition Approach for Speculative Firing Prediction in Distributed Saturation State-Space Generation

The saturation strategy for symbolic state-space generation is particularly effective for globally-asynchronous locally-synchronous systems. A distributed version of saturation, SaturationNOW, uses the overall memory available on a network of workstations to effectively spread the memory load, but its execution is essentially sequential. To achieve true parallelism, we explore a speculative firing prediction, where idle workstations work on predicted future event firing requests. A naïve approach where all possible firings may be explored a priori, given enough idle time, can result in excessive memory requirements. Thus, we introduce a history-based approach for firing prediction that recognizes firing patterns and explores only firings conforming to these patterns. Experiments show that our heuristic improves the runtime and has a small memory overhead.     

基于K-近邻法的股价曲线的模式识别分类研究

本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,具有良好的实时性和较高的针对性,实用性较强,并可进一步改进。     

基于神经网络的不变性模式识别的人脸识别系统

文章提出了一种优化的多层神经网络,能完成对图像不变性模式识别。基于此多层神经网络开发了人脸识别系统。仿真实验表明该人脸识别系统,具有较高的识别速度、准确率、容错性和鲁棒性,并且基本解决了开发实用化的人脸识别系统所面临的问题,即模式识别不变性的问题。     

机器人足球视觉目标的快速识别

本文基于YUV颜色空间,采用Minkowski距离对颜色进行分类;利用改进的差分法,对图像中的目标进行检测和识别,提出了一种识别机器人足球比赛中目标的快速算法,提高了系统的实时性,减少了程序的运算量。     

A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting

In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the financial indices of six different countries, and compare the results with neural networks on five different error measures. The results show that pattern recognition-based approaches in time-series forecasting are highly accurate, and that these are able to match the performance of advanced methods such as neural networks. Received: 2 April 1998?Received in revised form: 1 February 1999?Accepted: 16 February 1999     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

一种模块化神经网络结构用于模式识别

在模式识别中,通常直接用神经网络来处理复杂的多类分类问题,其识别的误判率较大。该文基于任务分解与模块整合的思想,提出了一个模块化Kohonen神经网络(KTD)结构用于模式分类,给出了其学习方法并做了模拟仿真,模拟仿真表明KTD能够获得较高的识别率且误判率较小。     

快时变汽温系统的线性加权参数辨识算法

本文根据火力发电厂变负荷机组的动态特性，提出了一种在线辨识快时变汽温系统的线性加权参数估计算法，并分析了算法的收敛特性。仿真研究表明了该算法的有效性。     

A Fuzzy Petri Net for Pattern Recognition: Application to Dynamic Classes

When involving evolutionary natural objects, the odeling of dynamic lasses is the main issue for a pattern recognition system. This problem an be avoided by making dynamic the syste of pattern recognition which an then enter into various states according to the evolution of the lasses. We propose a dynamic recognition system founded on two types of learning. The static aspect of the learning is ensured by lassifiers or systems of lassifiers, while the dynamic aspect is translated by the learning of the planning of the various states by a fuzzy Petri net. The method is sucessfully applied to a synthetic data set. Received 21 September 2000 / Revised 19 December 2000 / Accepted in revised form 1 March 2001     

A New Method of Selection and Reduction of System Feature in Pattern Recognition Based on Rough Sets

A new method of selection and reduction of system feature in pattern recognition based on rough sets is proposed in this paper. Its basic idea is that the classification ability of system feature is evaluated with the classification affection introduced by removing some redundant feature or unimportant feature in the system and comparing the final classification result to find out useful feature in pattern recognition. Eventually, reduction and optimum combination of feature sets are performed by this method. An example of selection and reduction of system feature in pattern recognition based on rough sets shows the correctness and effectiveness of the method.     

纸币号码快速识别系统

开发了一种快速的纸币号自动识别系统,能在捆钞的同时自动识别并记录下通过捆钞机上的纸币号码。该系统首先通过CCD摄像头采集24位彩色图像,经过逻辑脊谷判断法分割、投影法定位、结构法识别几个过程输出号码串。实验结果表明,该系统组织设计的逻辑脊谷判断算法有效提取了号码图像,穿越号码体距离法定位号码准确,提出的号码识别系统取得了较高的识别率。     

A cost-based fuzzy system for pattern classification with class importance

This paper proposes a cost-based fuzzy classification system for pattern classification problems with an order of class importance. The task here is to minimize the misclassification of patterns from an important class. It is assumed that the classification importance is given for each class, not for each pattern. Another assumption is that only the order of importance is given for given classes without any numerical measures of importance. We show the performance of the proposed cost-based fuzzy classification system for a real-world pattern classification problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

一种快速有效的印刷体文字识别算法

为了利用低成本的硬件来实现对印刷体文字的快速识别,提出了一种基于多级分类的印刷体文字快速识别算法,该算法从预处理、特征提取,到模式匹配各个阶段,都对传统方法作了合理的改进.该算法首先是采用36×36,而不是传统的48×48点阵进行归一化,从而有效地减少了计算量和字典容量;其次是采用改进的粗外围特征,并进行二重分割,以提高特征的稳定性;最后在各级分类中采用了不同的判别准则,包括绝对值距离、欧氏距离及相似度准则,以适应于时间、准确性的不同要求.同时用该算法对一级汉字7000个样本进行了实验,其结果表明,实际正确识别率(正识率)达95%,前5位累积正识率可达98%,从而为“电子阅读笔”的开发与研制打下了坚实的理论基础.     

A long trip in the charming world of graphs for Pattern Recognition

This paper is a historical overview of graph-based methodologies in Pattern Recognition in the last 40 years; history is interpreted with the aim of recognizing the rationale inspiring the papers published in these years, so as to roughly classify them. Despite the extent of scientific production in this field, it is possible to identify three historical periods, each having its own connotation common to most of the corresponding papers, which are called here as the pure, the impure and extreme periods.     

A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems

In this paper, a generalized adaptive ensemble generation and aggregation (GAEGA) method for the design of multiple classifier systems (MCSs) is proposed. GAEGA adopts an “over-generation and selection” strategy to achieve a good bias-variance tradeoff. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data globally with different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking into consideration both the ability of each ensemble to fit the validation data locally and reducing the risk of overfitting. In this paper, the performance of GAEGA is assessed experimentally in comparison with other multiple classifier aggregation methods on 16 data sets. The experimental results demonstrate that GAEGA significantly outperforms the other methods in terms of average accuracy, ranging from 2.6% to 17.6%.