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A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems 不同阿拉伯文手写文字识别系统组合的框架
H. E. Abed, V. Märgner
In this paper we present A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems to achieve a decision with a higher performance. This performance can be expressed by lower rejection rates and higher recognition rates. The used methods range from voting schemes based on results of different recognizer to a neural network decision based on normalized confidences. This work presents an extension of the well known combination methods for a large lexicon, an extension from maximum 30 classes (e.g., 10 classes for digits classification) to 937 classes for the IfN/ENIT-database. In addition, different reject rules based on the evaluation and analysis of individual and combined systems output are discussed. Different threshold function for reject levels are tested and evaluated. Tests with a set of recognizer, which participated in the ICDAR 2007 competition and based on set coming from the IfN/ENIT-database show that a word error rate (WER) of 5.29% without reject and with a reject rate less than 25% even a word error rate of less than 1%.
本文提出了一种结合不同阿拉伯语手写词识别系统的框架,以实现具有更高性能的决策。这种性能可以通过更低的拒绝率和更高的识别率来表达。使用的方法从基于不同识别器结果的投票方案到基于归一化置信度的神经网络决策。这项工作提出了一个众所周知的大型词典组合方法的扩展,从最多30个类(例如,10个类用于数字分类)扩展到IfN/ enit数据库的937个类。此外,还讨论了基于对单个系统和组合系统输出的评价和分析的不同拒绝规则。测试和评估了不同的拒绝水平阈值函数。使用参加ICDAR 2007竞赛的识别器集和IfN/ enit数据库的识别器集进行测试,结果表明,在不拒收的情况下,单词错误率(WER)为5.29%,在拒收率小于25%的情况下,单词错误率小于1%。
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引用次数: 1
The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications. 使用遗传规划学习三维颅面形状量化。
Indriyati Atmosukarto, Linda G Shapiro, Carrie Heike

Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].

颅面疾病通常导致各种头部形状畸形。这项工作的目标是量化患有22q11.2缺失综合征颅面疾病的个体在不同面部异常中表现出的各种3D形状变化。采用遗传规划(GP)来学习不同的三维形状量化。实验结果表明,GP方法比人类专家和现有计算机算法的分类率更高[1],[2]。
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引用次数: 10
QRS Complex Detection by Non Linear Thresholding of Modulus Maxima 模极大值非线性阈值法检测QRS复合体
B. Jalil, Ouadi Beya, E. Fauvet, O. Laligant
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引用次数: 0
Shape Filling Rate for Silhouette Representation and Recognition 轮廓表示与识别的形状填充率
Guocheng An, Fengjun Zhang, Hongan Wang, G. Dai
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引用次数: 4
A Combined Self-Configuring Method for Object Tracking in Colour Video 彩色视频中目标跟踪的组合自配置方法
Juan Alfonso Rosell Ortega, G. A. García, Ángel Rodas Jordá, Vicente Luis Atienza Vanacloig
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引用次数: 2
Locating People in Images by Optimal Cue Integration 通过最优线索整合在图像中定位人物
Vicente Luis Atienza Vanacloig, Juan Alfonso Rosell Ortega, G. A. García, J. M. González
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引用次数: 0
Time-series clustering by approximate prototypes 近似原型的时间序列聚类
Ville Hautamäki, Pekka Nykänen, P. Fränti
Clustering time-series data poses problems, which do not exist in traditional clustering in Euclidean space. Specifically, cluster prototype needs to be calculated, where common solution is to use cluster medoid. In this work, we define an optimal prototype as an optimization problem and propose a local search solution to it. We experimentally compare different time-series clustering methods and find out that the proposed prototype with agglomerative clustering followed by k-means algorithm provides best clustering accuracy.
对时间序列数据进行聚类会产生传统的欧氏空间聚类所不存在的问题。具体来说,需要计算集群原型,通常的解决方案是使用集群介质。在这项工作中,我们将最优原型定义为一个优化问题,并提出了一个局部搜索解决方案。实验比较了不同的时间序列聚类方法,发现基于k-means算法的聚类方法具有最佳的聚类精度。
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引用次数: 112
People and luggage recognition in airport surveillance under real-time constraints 在机场监控实时约束下的人员和行李识别
V. Atienza-Vanacloig, J. Rosell-Ortega, G. Andreu-García, J. Valiente-González
This paper describes an approach to classify people, groups of people and luggage in the halls of an airport. The algorithm is included into a surveillance system which tracks and classifies objects and transmits this information to a higher computational level which fuses the information of several cameras covering overlapping areas. Two kind of features are used: foreground density features and features related to real-size of objects, obtained by applying a homographic model. A classification schema based on k-nn classifiers and a voting system makes the classification process highly robust. On-line and off-line experiments are introduced.
本文介绍了一种对机场大厅里的人、人群和行李进行分类的方法。该算法用于跟踪和分类目标的监控系统,并将这些信息传递到更高的计算层,该计算层融合了覆盖重叠区域的多个摄像机的信息。采用了两种特征:前景密度特征和与物体实际尺寸相关的特征,这两种特征是通过应用同形模型获得的。基于k-nn分类器和投票系统的分类模式使分类过程具有高度鲁棒性。介绍了在线和离线实验。
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引用次数: 13
Background modelling in demanding situations with confidence measure 背景建模在苛刻的情况下与信心措施
J. Rosell-Ortega, G. Andreu-García, A. Rodas-Jordá, V. Atienza-Vanacloig
Background subtraction is a popular technique in video surveillance. In order to use it, a background model must be built and updated according to light and scenario changes. We discuss in this paper a new algorithm (BAC) which creates or restores a background model based on the behaviour of pixels in successive frames, while performs a segmentation of objects in the scene yielding a confidence value for the obtained background, a problem which is addressed by few methods in the literature. This allows us to fulfil the requirement of producing a model, for instance in scenarios like airport halls, without interfering normal operation and still segment scenes.
背景减法是视频监控中常用的一种技术。为了使用它,必须根据光线和场景的变化建立和更新背景模型。我们在本文中讨论了一种新的算法(BAC),该算法基于连续帧中像素的行为创建或恢复背景模型,同时对场景中的物体进行分割,为获得的背景产生置信度值,这是文献中很少有方法解决的问题。这使我们能够满足生产模型的要求,例如在机场大厅等场景中,而不会干扰正常操作和仍然分割场景。
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引用次数: 6
Computer-Aided Grading of Lymphangioleiomyomatosis (LAM) using HRCT. HRCT对淋巴管平滑肌瘤病(LAM)的计算机辅助分级。
Jianhua Yao, Nilo Avila, Andrew Dwyer, Angelo M Taveira-Dasilva, Olanda M Hathaway, Joel Moss

Lymphangioleiomyomatosis (LAM) is a multisystem disorder associated with proliferation of smooth muscle-like cells, which leads to destruction of lung parenchyma. Subjective grading of LAM on HRCT is imprecise and can be arduous especially in cases with severe involvement. We propose a computer-aided evaluation system that grades LAM involvement based on analysis of lung texture patterns. A committee of support vector machines is employed for classification. The system was tested on 36 patients. The computer grade demonstrates good correlation with subjective radiologist grade (R=0.91, p<0.0001) and pulmonary functional tests (R=0.85, p<0.0001). The grade also provides precise progression assessment of disease over time.

淋巴管平滑肌瘤病(LAM)是一种与平滑肌样细胞增生相关的多系统疾病,可导致肺实质破坏。在HRCT上对LAM的主观分级是不精确的,特别是在严重受累的情况下,可能会很困难。我们提出了一种基于肺结构模式分析的计算机辅助评估系统。一组支持向量机被用于分类。该系统在36名患者身上进行了测试。计算机评分与放射科医师主观评分具有良好的相关性(R=0.91, p
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引用次数: 12
期刊
Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition
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