Markov random field-based statistical character structure modeling for handwritten Chinese character recognition

This paper proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures, we design the neighborhood system that accounts for the most important stroke relationships. We penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials, and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system, we use the single-site likelihood clique potentials to extract many candidate strokes from character images, and use the pairsite clique potentials to determine the best structural match between the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the KAIST character database demonstrate that MRFs can statistically model character structures, and work well in the HCCR system.     

Safety assessment of structures in view of fuzzy randomness

Structural safety can be realistically assessed only if the uncertainty in the structural parameters is appropriately taken into consideration and realistic computational models are applied. Uncertainty must be accounted for in its natural form. Stochastic models are not always capable of fulfilling this task without restrictions, as uncertainty may also be characterized by fuzzy randomness or fuzziness. On the basis of the theory of the fuzzy random variables the fuzzy probabilistic safety concept is introduced and formulated as the fuzzy first order reliability method (FFORM). This concept permits fuzziness, randomness and fuzzy randomness to be accounted for simultaneously. FFORM is illustrated by way of an example; hereby, the influence of the computational model is also demonstrated.     

On-line handwritten Chinese character recognition via a fuzzy attribute representation

Chinese characters are constructed by strokes according to structural rules. Therefore, the geometric configurations of characters are important features for character recognition. In handwritten characters, stroke shapes and their spatial relations may vary to some extent. The attribute value of a structural identification is then a fuzzy quantity rather than a binary quantity. Recognizing these facts, we propose a fuzzy attribute representation (FAR) to describe the structural features of handwritten Chinese characters for an on-line Chinese character recognition (OLCCR) system. With a FAR. a fuzzy attribute graph for each handwritten character is created, and the character recognition process is thus transformed into a simple graph matching problem. This character representation and our proposed recognition method allow us to relax the constraints on stroke order and stroke connection. The graph model provides a generalized character representation that can easily incorporate newly added characters into an OLCCR system with an automatic learning capability. The fuzzy representation can describe the degree of structural deformation in handwritten characters. The character matching algorithm is designed to tolerate structural deformations to some extent. Therefore, even input characters with deformations can be recognized correctly once the reference dictionary of the recognition system has been trained using a few representative learning samples. Experimental results are provided to show the effectiveness of the proposed method.     

基于TS-MRF与模糊MLL模型的图像分割

基于树结构的马尔可夫随机场（TS-MRF）,提出模糊多级逻辑模型（fuzzy MLL）,并提出了一种新的图像分割算法——模糊TS-MRF算法。与传统的MRF分割算法和TS-MRF算法比较,该方法在计算耗时增加很少的情况下,对分割精度提高较大。更为重要的是,该方法提供了一个新思路,使得基于MRF的先验信息的描述更为精细。     

Hybrid probabilistic fuzzy and non-probabilistic model of structural reliability

This paper presents a new hybrid reliability model which contains randomness, fuzziness and non-probabilistic uncertainty based on the structural fuzzy random reliability and non-probabilistic set-based models. By solving the non-probabilistic set-based reliability problem and analyzing the reliability with fuzziness and randomness, the structural hybrid reliability can be obtained. The presented hybrid model has broad applicability which can handle either linear or non-linear state functions. A comparison among the presented hybrid model, probabilistic and non-probabilistic models, and the conventional probabilistic model is made through two typical numerical examples. The results show that the presented hybrid model, which may ensure structural security, is effective and practical.     

基于云模型的导航系统模糊可靠性评测分析

阐述基于云模型理论的飞行器导航系统模糊可靠性评测分析.云模型是一种新的实现 定性概念和定量数值之间转换的有力工具,用来统一刻划基于语言值的定性概念和数值表示之 间的相互映射关系.云的数字特征期望值Ex、熵En和超熵He三个数值表征,把模糊性和随机 性完全集成在一起,作为知识表示的基础.将其用于飞行器导航系统模糊可靠性分析及故障检 测,可有效地提高飞行器导航系统的可靠性分析.仿真结果说明了该分析方法的可行性和有效性.     

Image segmentation based on histogram analysis utilizing the cloud model

Both the cloud model and type-2 fuzzy sets deal with the uncertainty of membership which traditional type-1 fuzzy sets do not consider. Type-2 fuzzy sets consider the fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, and the association between them. Based on the cloud model, the paper proposes an image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type-2 fuzzy threshold, fuzzy C-means clustering, and Gaussian mixture models. The comparison experiments validate the proposed method.     

Learning templates from fuzzy examples in structural patternrecognition

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definitions into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situations. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric.     

基于模糊模式识别的巴布剂均匀度检测系统

为实现对巴布剂涂布过程中均匀度的检测,提出一种基于模糊模式识别的检测方法。根据采集图像像素点之间的空间和时间相关性及其特征界限的模糊性,引入模糊集理论,运用模糊算法对像素点的灰度值进行识别分类。检测系统采用基于CycloneⅡ系列的FPGA技术,运用Verilog HDL硬件语言对系统完成建模与实现,并且通过了仿真和验证。通过在线测试,对视频数据流进行分析、处理和识别,实现对涂布过程中巴布剂均匀度的检测,根据统计结果,正确率达到95%。检测结果证明了模糊模式识别算法的可行性和检测系统的可靠性。     

Type-2 fuzzy hidden Markov models and their application to speech recognition

This paper presents an extension of hidden Markov models (HMMs) based on the type-2 (T2) fuzzy set (FS) referred to as type-2 fuzzy HMMs (T2 FHMMs). Membership functions (MFs) of T2 FSs are three-dimensional, and this new third dimension offers additional degrees of freedom to evaluate the HMMs fuzziness. Therefore, T2 FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. We derive the T2 fuzzy forward-backward algorithm and Viterbi algorithm using T2 FS operations. In order to investigate the effectiveness of T2 FHMMs, we apply them to phoneme classification and recognition on the TIMIT speech database. Experimental results show that T2 FHMMs can effectively handle noise and dialect uncertainties in speech signals besides a better classification performance than the classical HMMs.     

基于数学形态学的模糊集理论在车牌字符识别中的运用

为了更加高效地利用模板匹配的方法实现对车牌字符图像的识别,结合数学形态学和模糊集理论,提出基于数学形态学的模糊模板匹配方法。首先,对于二值图像的每个像素点及其8-邻域,以赋权的方式刻画中心像素点隶属于字符的程度;其次,加4×4窗口选取代表点,并有重叠地遍历整个字符图像,以构造字符图像的模糊隶属度矩阵;进而运用海明贴近度计算待识别字符的归类,实现对字符的识别;最后,使用Matlab对模糊模板匹配方法进行编程,并在实际字符图像中测试识别效果。与传统模板匹配方法相比较,测试的结果表明,车牌字符的识别准确率得到了显著的提高。     

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.     

A fuzzy-Bayesian approach to target recognition based on multisensor fusion

The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology. Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness. However, in general, current Bayesian methods can only account for randomness. To accommodate complex natural problems with both types of uncertainties, it is profitable to improve the existing approach by incorporating fuzzy theory into classical techniques. In this paper, after representing both the individual attribute of the target in the model database and the sensor observation or report as the fuzzy membership function, a likelihood function is constructed to deal with fuzzy data collected by each sensor. A similarity measure is introduced to determine the agreement degree of each sensor. Based on the similarity measure, a consensus fusion approach (CFA) is developed to generate a global likelihood from the individual attribute likelihood for the whole sensor reports. A numerical example is illustrated to show the target recognition application of the fuzzy-Bayesian approach. The text was submitted by the authors in English.     

Recognition of handwritten Chinese characters by elastic matching

A method was proposed to match handwritten Chinese character patterns. Two given patterns are iteratively deformed until they match. An energy function and a neighborhood of influence is defined for each iteration. Initially a large neighborhood is used such that the movements result in large features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. The amount of computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. Extensive testing was carried out to evaluate the performance of the algorithm under various parameter settings. The method was applied to the recognition of handwritten Chinese characters with satisfactory results.     

STRUCTURED FUZZY MODELS

Abstract

The paper deals with a problem of modeling of fuzzy systems in random environments. A model is proposed that is capable of handling two distinct forms of imprecision, viz. randomness and fuzziness. The model is required to cope with both of them while modeling a variety of problems in management, medical diagnosis, and unsupervised pattern recognition. The models proposed in the paper are constructed and evaluated in a formal framework established by fuzzy relation equations. Randomness is introduced as additional constraints imposed on the structure of the fuzzy relation equation (hence: structured fuzzy models). The forecasting (prediction) problem is studied in detail.     

A novel stroke-based feature extraction for handwritten Chinese character recognition

A stroke-based approach to extract skeletons and structural features for handwritten Chinese character recognition is proposed. We first determine stroke directions based on the directional run-length information of binary character patterns. According to the stroke directions and their adjacent relationships, we split strokes into stroke and fork segments, and then extract the skeletons of the stroke segments called skeleton segments. After all skeleton segments are extracted, fork segments are processed to find the fork points and fork degrees. Skeleton segments that touch a fork segment are connected at the fork point, and all connected skeleton segments form the character skeleton. According to the extracted skeletons and fork points, we can extract primitive strokes and stroke direction maps for recognition. A simple classifier based on the stroke direction map is presented to recognize regular and rotated characters to verify the ability of the proposed feature extraction for handwritten Chinese character recognition. Several experiments are carried out, and the experimental results show that the proposed approach can easily and effectively extract skeletons and structural features, and works well for handwritten Chinese character recognition.     

类正态分布数据云模型的预测算法

类似正态分布在实际的生活与生产中分布最为广泛,精确确定的模糊概念隶属函数严重影响该类数据的预测精度.云模型把随机性和模糊性结合起来,用数字特征期望、熵和超熵,揭示随机性与模糊性的关联性.基于正态云模型设计预测算法,放宽形成正态分布要求的前提条件,把精确确定隶属函数放宽到构造正态隶属度分布的期望函数,更简单、直接地完成类正态分布的数据的预测,因而更具有普遍适用性.     

Cascade Markov random fields for stroke extraction of Chinese characters

Extracting perceptually meaningful strokes plays an essential role in modeling structures of handwritten Chinese characters for accurate character recognition. This paper proposes a cascade Markov random field (MRF) model that combines both bottom-up (BU) and top-down (TD) processes for stroke extraction. In the low-level stroke segmentation process, we use a BU MRF model with smoothness prior to segment the character skeleton into directional substrokes based on self-organization of pixel-based directional features. In the high-level stroke extraction process, the segmented substrokes are sent to a TD MRF-based character model that, in turn, feeds back to guide the merging of corresponding substrokes to produce reliable candidate strokes for character recognition. The merit of the cascade MRF model is due to its ability to encode the local statistical dependencies of neighboring stroke components as well as prior knowledge of Chinese character structures. Encouraging stroke extraction and character recognition results confirm the effectiveness of our method, which integrates both BU/TD vision processing streams within the unified MRF framework.     

基于模糊神经网络的水淹层自动识别

针对油田水淹层识别存在的模糊性和多解性,提出了一种基于模糊神经网络的水淹层自动识别方法。该方法将神经网络技术所具有的高度自适应性、容错性及固有的并行处理能力与模糊逻辑所具有的模拟人类思维中的模糊综合判别特点有机地结合,实现了多因素模糊综合判断推理来完成水淹层自动识别。采用该方法,对大庆油田135个地层样本进行处理,符合率达87．6％。结果表明该方法对解决水淹层自动识别问题具有良好的适应性,可提高水淹层自动识别的精度。     

Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF

The present research work deals with a logistic membership function (MF), within non-linear MFs, in finding out fuzziness patterns in disparate level of satisfaction for Multiple Criteria Decision-Making (MCDM) problem. This MF is a modified form of general set of S-curve MF. Flexibility of this MF in applying to real world problem has also been validated through a detailed analysis. An example illustrating an MCDM model applied in an industrial engineering problem has been considered to demonstrate the veracity of the proposed technique. The approach presented here provides feedback to the decision maker, implementer and analyst and gives a clear indication about the appropriate application and usefulness of the MCDM model. The key objective of this paper is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment.