复杂性测度在肌电信号模式识别中的应用

针对表面肌电信号(SEMG)的非平稳特性,提出了一种以复杂性测度和支持向量机(SVM)相结合的肌电信号模式识别新方法。肌电信号的复杂度作为一种新的肌电信号特征,算法简单。支持向量机是一种新的机器学习机制。通过对采集的四通道表面肌电信号进行分析,提取其复杂性测度信息构建特征矢量,利用“一对一”分类策略和二叉树构建的多类支持向量机分类器,很好地实现了对前臂的八种动作表面肌电信号的模式分类。实验表明,由支持向量机对肌电信号的复杂度特征进行分类,具有很好的稳定性和准确率,为肌电信号及其它非平稳生理电信号的模式分类提供了一种新思路。     

Complexity,Confusion, and Perceptual Grouping. Part II: Mapping Complexity

International Journal of Computer Vision - Intermediate-level vision is central to form perception, and we outline an approach to intermediate-level segmentation based on complexity analysis. In...     

The Digital Music Stand as a Minimal Processing Custom‐Made Optical Music Recognition System,Part 1: Key Music Symbol Recognition

The digital music stand is proposed as a minimal‐processing optical music recognition implementation, where music score (MS) presentation is realized without prior alignment, noise, or staff line removal. After each MS page is segmented into systems, staves, measures, and candidate music symbols, music symbol recognition is accomplished via probabilistic neural networks: Only the key music symbols (namely clefs, global accidentals, time signatures) of the MS are identified, while the remaining music symbols are generally classified. Subsequently, satisfactory quality of on‐screen MS viewing is accomplished via the concatenation and/or substitution of appropriately selected parts and isolated music symbols of the original MS. In this piece of research, the processing stages leading to on‐screen MS presentation are detailed.     

基于小波分析的模式识别的几个问题

小波分析是80年代末期、90年代初期发展起来的一种十分有效的分析工具,因其“自适应性”和“数字显微镜性质”而成为国际研究热点,至今仍方兴未艾。小波分析是以著名的Mallat离散小波算法和被广泛使用的Daubechies基作为技术核心,达到对信号、信息的分解与重构、特征提取、奇异性分析等。小波分析在图像处理方面的卓越效果使得人们对她倾注了大量心血,取得了许多研究成果。这里仅列举信号的分解与重构问题、钢的表面质量分析问题、混沌防伪编码问     

The Complexity of Finding Top-Toda-Equivalence-Class Members

We identify two properties that for P-selective sets are effectively computable. Namely, we show that, for any P-selective set, finding a string that is in a given length's top Toda equivalence class (very informally put, a string from that the set's P-selector function declares to be most likely to belong to the set) is computable, and we show that each P-selective set contains a weakly- -rankable subset.     

Multiple Contour Finding and Perceptual Grouping using Minimal Paths

We address the problem of finding a set of contour curves in an image. We consider the problem of perceptual grouping and contour completion, where the data is a set of points in the image. A new method to find complete curves from a set of contours or edge points is presented. Our approach is based on a previous work on finding contours as minimal paths between two end points using the fast marching algorithm (L. D Cohen and R. Kimmel, International Journal of Computer Vision, Vol. 24, No. 1, pp. 57–78, 1997). Given a set of key points, we find the pairs of points that have to be linked and the paths that join them. We use the saddle points of the minimal action map. The paths are obtained by backpropagation from the saddle points to both points of each pair.In a second part, we propose a scheme that does not need key points for initialization. A set of key points is automatically selected from a larger set of admissible points. At the same time, saddle points between pairs of key points are extracted. Next, paths are drawn on the image and give the minimal paths between selected pairs of points. The set of minimal paths completes the initial set of contours and allows to close them. We illustrate the capability of our approach to close contours with examples on various images of sets of edge points of shapes with missing contours.     

Interactive Pattern Recognition: Techniques and Systems

Pattern recognition is not a new subject. Its principles and methodologies have influenced the course of technological development for many years in almost every knowledge-based field. To some fields, pattern recognition is a major tool for problem- solving, capable of producing dramatic results. To others, it has been accompanied by failure and disappointment. Despite the mixed results, however, pattern recognition has continued to be a fascinating field of research and development in its own right. This is evidenced by the growing list of activities, academic and industrial alike, relating to pattern recognition work.     

感知归类在目标识别中的应用研究

感知归类是指人们在不拥有任何图像内容知识的前提下，从底层原始图像特征中提取有效的图像联系的一种视觉能力。本文着重介绍了感知归类方法在目标识别中的应用，通过使用感知归类中的接近、相似、连续等概念，逐步将底层的图像特征组合为高层的图像联系，直至获得有意义的语义表征。     

Pattern Recognition in Micro-Economics

A property which is intrinsic to the field of economics is the idea of aggregation. From the concept of a utility function which arose through the combining of wants or needs to input-output studies the concept of aggregation plays an important part in the study of economic systems-both micro and macro. Even though aggregation plays an important role in economics its explicit use is limited to the manipulation of statistical data. The technique of pattern recognition also involves the problem of aggregation. In fact pattern recognition is aggregation. This is quite obvious when we see that in pattern recognition processes large quantities of data are classified as belonging to various predetermined pattern classes. This paper will deal with aggregation in a particular area of consumer choice by using an elementary idea of pattern reognition The particular problem that we shall be dealing with is that of transitivity in individual decision procedures. We shall approach this particular problem through the idea of a generalized characteristic function of a set–a fuzzy set.     

Complexity, Confusion, and Perceptual Grouping. Part I: The Curve-like Representation

Intermediate-level vision is central to form perception, and we outline an approach to intermediate-level segmentation based on complexity analysis. We focus on the problem of edge detection, and how edge elements might be grouped together. This is typical because, once the local structure is established, the transition to global structure must be effected and context is critical. To illustrate, consider an edge element inferred from an unknown image. Is this local edge part of a long curve, or part of a texture? If the former, which is the next element along the curve? If the latter, is the texture like a hair pattern, in which nearby elements are oriented similarly, or like a spaghetti pattern, in which they are not? Are there other natural possibilities? Such questions raise issues of dimensionality, since curves are 1-D and textures are 2-D, and also of complexity. Working toward a measure of representational complexity for vision, in this first of a pair of papers we develop a foundation based on geometric measure theory. The main result concerns the distribution of tangents in space and in orientation, which serves as a formal basis for the concrete measure of representational complexity developed in the companion paper.     

Complexity,Confusion, and Perceptual Grouping. Part I: The Curve-like Representation

International Journal of Computer Vision - Intermediate-level vision is central to form perception, and we outline an approach to intermediate-level segmentation based on complexity analysis. We...     

音乐情感识别研究综述

音乐是表达情感的重要载体,音乐情感识别广泛应用于各个领域.当前音乐情感研究中,存在音乐情感数据集稀缺、情感量化难度大、情感识别精准度有限等诸多问题,如何借助人工智能方法对音乐的情感趋向进行有效的、高质量的识别成为当前研究的热点与难点.总结目前音乐情感识别的研究现状,从音乐情感数据集、音乐情感模型、音乐情感分类方法三方面...     

Complexity Issues in Axiomatic Extensions of Lukasiewicz Logic

In this article, the computational complexity of all axiomaticextensions of ukasiewicz propositional logic and the arithmeticalcomplexity of both the general and standard semantics of theircorresponding predicate logics is determined.     

Instantaneous Learning in Pattern Recognition

Automation and Remote Control - One of the main disadvantages of artificial neural networks is slow learning associated with the need to calculate a large number of coefficients. The article shows...     

乐谱识别中的结构拆分及图象恢复

在电子出版及许多音乐研究中，均需将原文乐谱转化为被计算机可读的数据。本文提出一种用字符识别方法的乐谱图象识别系统。它基于拆分结构技术，将原文转换为局部结构图切分乐符，有效地减少了数据量，且不受图象弯曲和倾斜的影响。文中给出了识别结果的文本输出及其图象恢复。     

模式识别与机器学习

<正>内容简介本书系统介绍了模式识别与机器学习的基础理论、模型与算法,兼顾前沿知识的融入。以贝叶斯学习思想贯穿始终,并适时与其他重要知识点(如支持向量机、深度学习等)进行交叉和关联,便于读者在形成良好知识体系的同时保持对整个领域知识的把握。全书共14章和4个附录,循序渐进地剖析模式识别与机器学习领域。首先介绍贝叶斯学习基础、逻辑回归、概率图模型基础、隐马尔可夫模型和条件随机场,