一个简单高效的动态手势识别方法

文章首先给出了基于角度的动力学模型及其特征值,并提出了基于SCG神经网络的静态特征值识别算法和基于模板匹配的动态特征值识别算法。使用该文提出的动态时间规整算法和手势分割算法建立的动态手势识别系统,实践证明具有较好的实时性和识别率。     

基于分位数的手写汉字识别方法

以往的手写汉字识别方法,无论应用何种特征提取方法,在生成标准模板时,一般都采用样本特征的算术平均值。文章提出了一种使用样本特征的分位数组合生成标准模板的方法,通过其在手写汉字识别中的应用表明,该方法比基于均值的标准模板有更好的鲁棒性,且在不增加任何计算量和算法复杂度的前提下,使系统的识别性能有所提高;同时该算法还有很好的推广性能,可以应用到各种特征提取算法中。     

手写中文速记符中音符的动态规划识别方法

本文在分析英文速记识别技术以及中文速记特点的基础上，提出了中文速记符的自动识别策略，并且以“人群速记”体系为研究对象，详细描述了用于识别速记符中297个音符的动态规划识别过程。通过采用局部平滑预处理，以及基于速记符形状特征和结构特征的粗分类措施，大大提高了动态规划识别速度和正确识别率。初步实验表明，对特定人书写的297个人群速记音符用动态规划法进行识别，正确识别率能达到93％以上。     

On-line signature recognition based on VQ-DTW

This paper studies some pattern recognition algorithms for on-line signature recognition: vector quantization (VQ), nearest neighbor (NN), dynamic time warping (DTW) and hidden Markov models (HMM). We have used a database of 330 users which includes 25 skilled forgeries performed by five different impostors. This database is larger than the typical ones found in the literature.Experimental results reveal that our first proposed combination of VQ and DTW (by means of score fusion) outperforms the other algorithms (DTW, HMM) and achieves a minimum detection cost function (DCF) value equal to 1.37% for random forgeries and 5.42% for skilled forgeries. In addition, we present another combined DTW-VQ scheme which enables improvement of privacy for remote authentication systems, avoiding the submission of the whole original dynamical signature information (using codewords, instead of feature vectors). This system achieves similar performance than DTW.     

A New Hybrid Approach to Handwritten Address Verification

The use of optical character recognition (OCR) has achieved considerable success in the sorting of machine-printed mail. The automatic reading of unconstrained handwritten addresses however, is less successful. This is due to the high error rate caused by the wide variability of handwriting styles and writing implements. This paper describes a strategy for automatic handwritten address reading which integrates a postcode recognition system with a hybrid verification stage. The hybrid verification system seeks to reduce the error rate by correlating the postcode against features extracted and words recognised from the remainder of the handwritten address. Novel use of syntactic features extracted from words has resulted in a significant reduction in the error rate while keeping the recognition rate high. Experimental results on a testset of 1,071 typical Singapore addresses showed a significant improvements from 24.0% error rate, 71.2% correct recognition rate, and 4.8% rejection rate using raw OCR postcode recognition to 0.4% error rate, 65.1% correct recognition rate, and 34.5% rejection rate using the hybrid verification approach. The performance of the approach compares favourably with the currently installed commercial system at Singapore Post, which achieved 0.7% error rate, 47.8% correct recognition rate, and 51.5% rejection rate for 6-digit postcode using the same test data.     

A Neural Network Approach to Planar-Object Recognition in 3D Space

Most existing 2D object recognition algorithms are not perspective (or projective) invariant, and hence are not suitable for many real-world applications. By contrast, one of the primary goals of this research is to develop a flat object matching system that can identify and localise an object, even when seen from different viewpoints in 3D space. In addition, we also strive to achieve good scale invariance and robustness against partial occlusion as in any practical 2D object recognition system. The proposed system uses multi-view model representations and objects are recognised by self-organised dynamic link matching. The merit of this approach is that it offers a compact framework for concurrent assessments of multiple match hypotheses by promoting competitions and/or co-operations among several local mappings of model and test image feature correspondences. Our experiments show that the system is very successful in recognising object to perspective distortion, even in rather cluttered scenes. Receiveed: 29 May 1998?,Received in revised form: 12 October 1998?Accepted: 26 October 1998     

用LPC及DTW进行语音模式比较的设计与实现

阐述了提取LPC特征参数和DTW模式匹配基本原理,并进一步实现了求解LPC的杜宾算法和有效进行DTW的一个算法。在此基础上,根据英语学习的实际需要,设计并实现了一个语音模式比较实例。     

虹膜识别中噪声的检测和处理方法

针对虹膜识别中存在的问题,在识别时应该尽可能将虹膜有效信息提取出来,而将眼皮、睫毛、光点的反射等去除,同时在特征提取中,提出了通过噪卢扩展方法消除噪声信号在编码过程中带来的影响。实验结果表明该文提出的方法有助于提高识别的可靠性和模式分类能力。     

Scaling and time warping in time series querying

The last few years have seen an increasing understanding that dynamic time warping (DTW), a technique that allows local flexibility in aligning time series, is superior to the ubiquitous Euclidean distance for time series classification, clustering, and indexing. More recently, it has been shown that for some problems, uniform scaling (US), a technique that allows global scaling of time series, may just be as important for some problems. In this work, we note that for many real world problems, it is necessary to combine both DTW and US to achieve meaningful results. This is particularly true in domains where we must account for the natural variability of human actions, including biometrics, query by humming, motion-capture/animation, and handwriting recognition. We introduce the first technique which can handle both DTW and US simultaneously, our techniques involve search pruning by means of a lower bounding technique and multi-dimensional indexing to speed up the search. We demonstrate the utility and effectiveness of our method on a wide range of problems in industry, medicine, and entertainment.     

A Real-Time Approach to the Spotting, Representation, and Recognition of Hand Gestures for Human–Computer Interaction

Aiming at the use of hand gestures for human–computer interaction, this paper presents a real-time approach to the spotting, representation, and recognition of hand gestures from a video stream. The approach exploits multiple cues including skin color, hand motion, and shape. Skin color analysis and coarse image motion detection are joined to perform reliable hand gesture spotting. At a higher level, a compact spatiotemporal representation is proposed for modeling appearance changes in image sequences containing hand gestures. The representation is extracted by combining robust parameterized image motion regression and shape features of a segmented hand. For efficient recognition of gestures made at varying rates, a linear resampling technique for eliminating the temporal variation (time normalization) while maintaining the essential information of the original gesture representations is developed. The gesture is then classified according to a training set of gestures. In experiments with a library of 12 gestures, the recognition rate was over 90%. Through the development of a prototype gesture-controlled panoramic map browser, we demonstrate that a vocabulary of predefined hand gestures can be used to interact successfully with applications running on an off-the-shelf personal computer equipped with a home video camera.     

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     

Shape-based word recognition

We describe a process of word recognition that has high tolerance for poor image quality, tunability to the lexical content of the documents to which it is applied, and high speed of operation. This process relies on the transformation of text images into character shape codes, and on special lexica that contain information on the shape of words. We rely on the structure of English and the high efficiency of mapping between shape codes and the characters in the words. Remaining ambiguity is reduced by template matching using exemplars derived from surrounding text, taking advantage of the local consistency of font, face and size as well as image quality. This paper describes the effects of lexical content, structure and processing on the performance of a word recognition engine. Word recognition performance is shown to be enhanced by the application of an appropriate lexicon. Recognition speed is shown to be essentially independent of the details of lexical content provided the intersection of the occurrences of words in the document and the lexicon is high. Word recognition accuracy is dependent on both intersection and specificity of the lexicon. Received May 1, 1998 / Revised October 20, 1998     

A Set of Neural Tools for Human-Computer Interactions: Application to the Handwritten Character Recognition, and Visual Speech Recognition Problems

This paper presents a new technique of data coding and an associated set of homogenous processing tools for the development of Human Computer Interactions (HCI). The proposed technique facilitates the fusion of different sensorial modalities and simplifies the implementations. The coding takes into account the spatio-temporal nature of the signals to be processed in the framework of a sparse representation of data. Neural networks adapted to such a representation of data are proposed to perform the recognition tasks. Their development is illustrated by two examples: one of on-line handwritten character recognition; and the other of visual speech recognition.     

基于关键路径的工作流时间异常处理算法

为了更好地管理业务流程中的时间异常,定义了时间异常系数,描述了不同时间约束异常之间的相互影响,并提出一种基于关键路径的工作流时间异常处理方法。在实例执行阶段,实时监控每个节点的状态,当出现时间约束违反时,根据异常系数决定是否触发异常处理程序以及如何处理。该方法能够在某一时间异常发生后,预测可能出现的关联时间异常并排除,确保工作流实例状态的一致性。     

A Neural Network Approach to Predicting Airspeed in Helicopters

A helicopter’s airspeed and sideslip angle is difficult to measure at speeds below 50 knots. This paper describes the application of Artificial Neural Network (ANN) techniques to the helicopter low air-speed problem. Three ANN methods were applied to the problem: a linear network, a Radial Basis Function (RBF) network, and a Multi-Layer Perceptron (MLP), trained using an implementation of the Levenberg–Marquardt (L–M) algorithm. Internally available measurements, such as control positions and body attitudes and rates, were generated using a realistic simulation model of a Lynx helicopter. These measurements formed the inputs to the ANN methods. The MLP was found to be the superior method. Further testing, including a Tagu-chi analysis, indicated the validity of the method. It is concluded that ANN techniques present a promising solution to the helicopter low airspeed problem.     

工作流管理系统中一种概率性分析和调整时间约束的方法

时间管理是工作流管理系统应提供的重要功能,其中一个关键问题是分析时间约束的可行性,并调整时间约束以避免可能出现的违反.然而,在高度不确定的工作流环境下,传统定性的时间约束分析结果过于严格.因此,提出概率时间约束工作流网来描述时间约束下的工作流流程.基于该网提出一种方法,以随机的形式分析活动满足时间约束的概率.该概率能帮助流程管理者灵活地分析时间约束的可行性,进一步为时间约束调整提供精确的指导.此外,用一个实际工作流管理系统中的例子验证工作的有效性.     

一种基于颜色分块特征的自适应图象检索方法

文章提出了一种基于颜色特征的,结合分块和遗传算法的图像检索方法。该方法充分利用了分块的特点,改善了颜色特征缺乏空间信息的缺点,同时利用遗传算法的自适应性,减少了在相关反馈的检索过程中,用户的选择操作。文章通过比较实验,证明了这种方法的有效性,并提出了进一步改进的方案。     

Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters

This paper describes an adaptive recognition system for isolated handwritten characters and the experiments carried out with it. The characters used in our experiments are alphanumeric characters, including both the upper- and lower-case versions of the Latin alphabets and three Scandinavian diacriticals. The writers are allowed to use their own natural style of writing. The recognition system is based on the k-nearest neighbor rule. The six character similarity measures applied by the system are all based on dynamic time warping. The aim of the first experiments is to choose the best combination of the simple preprocessing and normalization operations and the dissimilarity measure for a multi-writer system. However, the main focus of the work is on online adaptation. The purpose of the adaptations is to turn a writer-independent system into writer-dependent and increase recognition performance. The adaptation is carried out by modifying the prototype set of the classifier according to its recognition performance and the user's writing style. The ways of adaptation include: (1) adding new prototypes; (2) inactivating confusing prototypes; and (3) reshaping existing prototypes. The reshaping algorithm is based on the Learning Vector Quantization. Four different adaptation strategies, according to which the modifications of the prototype set are performed, have been studied both offline and online. Adaptation is carried out in a self-supervised fashion during normal use and thus remains unnoticed by the user. Received June 30, 1999 / Revised September 29, 2000