Local context-based recognition of sketched diagrams

We present a new methodology aimed at the design and implementation of a framework for sketch recognition enabling the recognition and interpretation of diagrams. The diagrams may contain different types of sketched graphic elements such as symbols, connectors, and text. Once symbols are distinguished from connectors and identified, the recognition proceeds by identifying the local context of each symbol. This is seen as the symbol interface exposed to the rest of the diagram and includes predefined attachment areas on each symbol. The definition of simple constraints on the local context of each symbol allows to greatly simplify the definition of the visual grammar, which is used only for further refinement and interpretation of the set of acceptable diagrams. We demonstrate the potential of the methodology using flowcharts and binary trees as examples.     

Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems

This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.     

A framework for human factors evaluation

Successful human factors evaluation of interactive computer systems has tended to rely heavily on the experience of the practitioner, who has had little explicit support on which to draw. This paper concerns support for evaluation in the form of a framework for describing and guiding the general activity. The paper starts with a critique of current approaches to evaluation, and particularly of evaluation within the 'design for usability' approach. Following a definition of evaluation, a framework is proposed that attempts to clarify what can be done towards which goals and how it can be done. This highlights and discusses notions of system performance, of assessment statements, and of assessment methods. The paper concludes with a discussion of the implications of the framework for evaluation practice.     

A graph grammar programming style for recognition of music notation

Graph grammars are a promising tool for solving picture processing problems. However, the application of graph grammars to diagram recognition has been limited to rather simple analysis of local symbol configurations. This paper introduces the Build-Weed-Incorporate programming style for graph grammars and shows its application in determining the meaning of complex diagrams, where the interaction among physically distant symbols is semantically important. Diagram recognition can be divided into two stages: symbol recognition and high-level recognition. Symbol recognition has been studied extensively in the literature. In this work we assume the existence of a symbol recognizer and use a graph grammar to assemble the diagram's information content from the symbols and their spatial relationships. The Build-Weed-Incorporate approach is demonstrated by a detailed discussion of a graph grammar for high-level recognition of music notation. See Appendix A for an illustration of the terms for musical symbols used in this paper.     

Sketched symbol recognition with auto-completion

Sketching is a natural mode of communication that can be used to support communication among humans. Recently there has been a growing interest in sketch recognition technologies for facilitating human–computer interaction in a variety of settings, including design, art, and teaching. Automatic sketch recognition is a challenging problem due to the variability in hand drawings, the variation in the order of strokes, and the similarity of symbol classes. In this paper, we focus on a more difficult task, namely the task of classifying sketched symbols before they are fully completed. There are two main challenges in recognizing partially drawn symbols. The first is deciding when a partial drawing contains sufficient information for recognizing it unambiguously among other visually similar classes in the domain. The second challenge is classifying the partial drawings correctly with this partial information. We describe a sketch auto-completion framework that addresses these challenges by learning visual appearances of partial drawings through semi-supervised clustering, followed by a supervised classification step that determines object classes. Our evaluation results show that, despite the inherent ambiguity in classifying partially drawn symbols, we achieve promising auto-completion accuracies for partial drawings. Furthermore, our results for full symbols match/surpass existing methods on full object recognition accuracies reported in the literature. Finally, our design allows real-time symbol classification, making our system applicable in real world applications.     

Does the cost function matter in Bayes decision rule?

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.     

Music symbol recognition by a LAG-based combination model

Most of optical music recognition (OMR) systems work under the assumption that the input image is scanner-based. However, we propose in this paper, camera based OMR system. Camera based OMR has a challengeable work in un-controlled environment such as a light, perspective, curved, transparency distortions and uneven staff-lines which tend to incur more frequently. In addition, the loss in performance of binarization methods, line thickness variation and space variation between lines are inevitable. In order to solve these problems, we propose a novel and effective staff-line removal method based on following three main ideas. First, a state-of-the-art staff-line detection method, Stable Path, is used to extract staff-line skeletons of the music score. Second, a line adjacency graph (LAG) model is exploited in a different manner over segmentation to cluster pixel runs generated from the run-length encoding (RLE) of an music score image. Third, a two-pass staff-line removal pipeline called filament filtering is applied to remove clusters lying on the staff-line. A music symbol is comprised of several parts so-called primitives, but the combination of these parts to form music symbol is unlimited. It causes difficulty applying the state-of-the-art method for music symbol recognition. To overcome these challenges and deal with primitive parts separately, we proposed a combination model which consists of LAG model, Graph model, and Set model as a framework for music symbol recognition. Our method shows impressive results on music score images captured from cameras, and gives high performance when applied to the ICDAR/GREC 2013 database, and a Gamera synthetic database. We have compared to some commercial software and proved the expediency and efficiency of the proposed method.     

基于形态学的数字符号图像骨架的识别方法研究

在数字符号图像骨架的识别中,对于数字符号图像骨架的多样性和复杂性,尚缺乏一种准确和通用的识别方法,对此提出了基于形态学的数字符号图像骨架的特征值提取方法。该方法定义了骨架中的端点、节点等概念,利用数学形态学的方法识别图像骨架。计算机仿真表明,该方法不仅具有通用性,而且具有很高的准确性。     

基于跨场景推理的事件关系检测方法

事件关系检测是一项面向事件之间逻辑关系的自然语言处理技术。事件关系识别的核心任务是以事件为基本语义单元,通过分析事件的篇章结构信息及语义特征,实现事件逻辑关系的深层检测。该文首次建立一套事件关系检测的任务和研究体系,包括任务定义、关系体系划分、语料采集与标注、评价方法等。同时,该文提出了一种跨场景推理的事件关系检测方法,该方法认为,具有相同事件场景的“事件对”,往往具有相同的事件关系类型。该文提出的基于跨场景推理的事件关系检测方法在针对四大类事件关系类型的检测精确率为54.21%。     

The Total Variation of Bounded Variation Functions to Evaluate and Rank Fuzzy Quantities

In this paper, we present a different approach to introduce evaluation and ranking of fuzzy quantities. These general fuzzy sets are obtained by the union of several fuzzy sets. They are neither normal nor convex. The idea we have followed is to use the total variation and the bounded variation function definitions applied to the membership function of a fuzzy set to introduce its evaluation. This approach has produced that the well‐known method of area compensation, introduced by Fortemps and Roubens only in a geometrical framework, is now presented in a general contest and useful for any fuzzy set. Moreover, this new representation formula provides an α‐cut view. This aspect, absent in Fortemps and Roubens paper, offers an evaluation by a weighted average of alfa‐cuts values, where the weights are connected with the number of subintervals that produce every α‐cut. Following the same idea, we have introduced the ambiguity definition of a general fuzzy set. By this new definition of evaluation and the consequent ambiguity, we present a way to rank fuzzy quantities.     

Towards the improved treatment of generalization of knowledge claims in IS research: drawing general conclusions from samples

This paper presents a framework for justifying generalization in information systems (IS) research. First, using evidence from an analysis of two leading IS journals, we show that the treatment of generalization in many empirical papers in leading IS research journals is unsatisfactory. Many quantitative studies need clearer definition of populations and more discussion of the extent to which ‘significant’ statistics and use of non-probability sampling affect support for their knowledge claims. Many qualitative studies need more discussion of boundary conditions for their sample-based general knowledge claims. Second, the proposed new framework is presented. It defines eight alternative logical pathways for justifying generalizations in IS research. Three key concepts underpinning the framework are the need for researcher judgment when making any claim about the likely truth of sample-based knowledge claims in other settings; the importance of sample representativeness and its assessment in terms of the knowledge claim of interest; and the desirability of integrating a study's general knowledge claims with those from prior research. Finally, we show how the framework may be applied by researchers and reviewers. Observing the pathways in the framework has potential to improve both research rigour and practical relevance for IS research.     

基于自动机原理的钢筋标识串的自动识别

钢筋量的统计是工程预算中的一个重要组成部分.传统的钢筋量统计方法存在效率低、易出错等缺点.而利用计算机自动识别完成钢筋量的统计可以很好解决上述缺点.本文基于形式语言与自动机理论,主要对钢筋标识串的自动识别进行了研究.首先对钢筋标识串给出了递归的定义,并构造出了相应的文法;其次,设计出了一台确定性有限自动机,对钢筋标识串所包含的信息进行自动识别.提高了识别的正确性、有效性,为钢筋量的自动统计提供了有力的基础.     

A unified tensor framework for face recognition

In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion.     

A methodological approach for combining super-resolution and pattern-recognition to image identification

Image acquisition systems integrated with laboratory automation produce multi-dimensional datasets. An effective computational approach for automatic analysis of image datasets is given by pattern recognition methods; in some cases, it can be advantageous to accomplish pattern recognition with image super-resolution procedures. In this paper, we define a method derived from pattern recognition techniques for the recognition of artefacts and noise on set of images combined with super resolution algorithms. The advantage of our approach is automatic artefacts recognition, opening the possibility to build a general framework for artefact recognition independently by the specific application where it is used.     

数字水印技术研究进展

随着因特网的日益普及,多媒体信息交流达到了前所未有的深度和广度,但作品侵权也随之更加容易,篡改也更加方便,因此,如何保护作品版权已受到人们的高度重视,而如今信息隐藏技术,特别是数字水印技术作为版权保护的重要手段,已得到广泛的研究和应用,为了使人们对该技术有一个较全面的了解,因而对数字水印技术的研究与进展情况进行了较系统的论述,即首先对信息隐藏技术进行了介绍,并对该技术进行了分类,然后重点分析了数字水印技术的模型、典型算法及其攻击方法,最后对数字水印技术的研究发展及其应用前景指出了一些可能的方向。     

一种文本相似度及其在语音识别中的应用

随着语音识别研究的深入,提高通用识别引擎的精度变得越来越困难。但对具体的语音识别任务,结合相应的背景,采取相应的措施,有可能达到很理想的识别精度。在已知语音输入为某有限集元素之一的情形,利用文本在发音上的相似度可以大大提高识别的精度。本文对原有文本相似度的定义进行了改进与完善,并就其在语音识别任务中的作用进行了深入的研究。     

An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.     

A set‐oriented method definition language for object databases and its semantics

In this paper we propose a set‐oriented rule‐based method definition language for object‐oriented databases. Most existing object‐oriented database systems exploit a general‐purpose imperative object‐oriented programming language as the method definition language. Because methods are written in a general‐purpose imperative language, it is difficult to analyze their properties and to optimize them. Optimization is important when dealing with a large amount of objects as in databases. We therefore believe that the use of an ad hoc, set‐oriented language can offer some advantages, at least at the specification level. In particular, such a language can offer an appropriate framework to reason about method properties. In this paper, besides defining a set‐oriented rule‐based language for method definition, we formally define its semantics, addressing the problems of inconsistency and non‐determinism in set‐oriented updates. Moreover, we characterize some relevant properties of methods, such as conflicts among method specifications in sibling classes and behavioral refinement in subclasses. Copyright © 2003 John Wiley & Sons, Ltd.     

手写体汉字识别问题综论

本文在讨论了人对汉字的认知心理试验研究结果之后,认为人类的汉字识别是经由整字属性及分层结构的多种途径复合而成的。这些途径中的大多数都可以在手写体汉字的机器识别中模仿运用。由此可把汉字的机器识别方法归纳为整模式法和子模式法两个大类,对它们的有利方面和困难方面进行了讨论;对影响其性能的因素进行了分析;对识别率以及它与识别字集大小和后处理等的关系提出了评估的问题。