In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals withthe scalogram of the differential turning angle. We...
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ISBN:
(纸本)9783540698111
In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals withthe scalogram of the differential turning angle. We then showed that this representation is rotation, translation and scale change invariant and that it is also shearing and noise resistant. In this paper, we propose some features extracted from this scalogram. this enables us to evaluate the turning angle scalogram representation of planar objects in the context of patternrecognition. When applied to shape retrieval from a database and for various transformations (deformations), experimental results confirm the efficiency of this new description approach.
In the field of patternrecognition, the design of an efficient decoding algorithm is critical for statistical machine translation. the most common statistical machine translation decoding algorithms use the concept o...
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ISBN:
(纸本)3540225706
In the field of patternrecognition, the design of an efficient decoding algorithm is critical for statistical machine translation. the most common statistical machine translation decoding algorithms use the concept of partial hypothesis. Typically, a partial hypothesis is composed by a subset of source positions, which indicates the words that have been translated in this hypothesis, and a prefix of the target sentence. thus, the target sentence is generated from left to right obtaining source words in an arbitrary order. We present a new approach, where the source sentence is translated from left to right and the possible word reordering is performed at the target prefix. We implemented this approach using a multi-stack decoding technique for a phrase-based model, and compared it with both a conventional approach and a monotone approach. Our experiments show how the new approach can significantly reduce the search time without increasing the search errors.
Forensic palmprint recognition, which mainly deals with high-resolution palmprints and latent-to-full palmprint comparison, has aroused research highlights because of the increased use of the evidence of palmprints in...
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For the novelties or anomalies of faulty signals occur in a damage circuit and fault signals vary with different circuit damages. To ensure the accuracy, and reliability of diagnosis, it is very important to extract t...
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ISBN:
(纸本)9781424410651
For the novelties or anomalies of faulty signals occur in a damage circuit and fault signals vary with different circuit damages. To ensure the accuracy, and reliability of diagnosis, it is very important to extract the characteristic features of fault signals. Two feature extraction methods based on wavelet packet transform is proposed to treat transient signals: optimal wavelet packet transform (OWPT) and incomplete wavelet packet transform (IWPT). For the fault signals decomposed, the energy in each frequency band may be heightened or be reduced, so a novel 'energy-fault' method is put forward to extract fault features. the problem of fault diagnosis of analog circuit is actually a patternrecognition problem. Nowadays, the binary free support v;ector machines (BTSVMs) is usually used for multi-class classification, but the structure of the binary tree is closely related to the classification performance of binary tree support vector machines (BTSVMs). A new separability measure method based on the space distribution of pattern classes is applied to construct different binary trees. three BTSVMs classifiers based on the separability measure are defined in this paper: inclined binary tree Support vector machines (IBTSVMs), balanced binary tree support vector machines (BBTSVMs) and adaptive binary tree support vector machines (ABTSVMs). Simulation results show us that the OWPT method is prefect for soft fault diagnosis, the IWPT for hard fault diagnosis, and the BBTSVMs multi-classifier possesses better classification speed, the ABTSVMs multi-classifer better classification accuracy.
the proceedings contain 56 papers. the special focus in this conference is on Machine Learning, Probability and Topology. the topics include: Pruning for monotone classification trees;regularized learning with flexibl...
ISBN:
(纸本)3540408134
the proceedings contain 56 papers. the special focus in this conference is on Machine Learning, Probability and Topology. the topics include: Pruning for monotone classification trees;regularized learning with flexible constraints;learning to answer emails;a semi-supervised method for learning the structure of robot environment interactions;using domain specific knowledge for automated modeling;resolving rule conflicts with double induction;a novel partial-memory learning algorithm based on grey relational structure;constructing hierarchical rule systems;text categorization using hybrid multiple model schemes;learning dynamic bayesian networks from multivariate time series with changing dependencies;topology and intelligent data analysis;coherent conditional probability as a measure of information of the relevant conditioning events;very predictive ngrams for space-limited probabilistic models;learning linear classifiers sensitive to example dependent and noisy costs;an effective associative memory for patternrecognition;similarity based classification;numerical attributes in decision trees;similarity-based neural networks for applications in computational molecular biology;combining pairwise classifiers with stacking and adapting association rule learning to subgroup discovery.
the 13thinternationalconference on Human–Computer Interaction, HCI Inter- tional 2009, was held in San Diego, California, USA, July 19–24, 2009, jointly withthe Symposium on Human Interface (Japan) 2009, the 8th ...
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ISBN:
(数字)9783642027130
ISBN:
(纸本)9783642027123
the 13thinternationalconference on Human–Computer Interaction, HCI Inter- tional 2009, was held in San Diego, California, USA, July 19–24, 2009, jointly withthe Symposium on Human Interface (Japan) 2009, the 8thinternationalconference on Engineering Psychology and Cognitive Ergonomics, the 5thinternationalconference on Universal Access in Human-Computer Interaction, the third international Conf- ence on Virtual and Mixed Reality, the third internationalconference on Internati- alization, Design and Global Development, the third internationalconference on Online Communities and Social Computing, the 5thinternationalconference on Augmented Cognition, the Second internationalconference on Digital Human Mod- ing, and the First internationalconference on Human Centered Design. A total of 4,348 individuals from academia, research institutes, industry and gove- mental agencies from 73 countries submitted contributions, and 1,397 papers that were judged to be of high scientific quality were included in the program. these papers - dress the latest research and development efforts and highlight the human aspects of the design and use of computing systems. the papers accepted for presentation thoroughly cover the entire field of human–computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.
Computational methods for gene identification in genomic sequences typically have two phćises: coding region prediction and gene parsing. While there are many effective methods for predicting coding regions {exons), p...
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In this paper, a novel technique has been introduced for 3D face recognition based on the modified local binary pattern extracted from a 3D range image. the new LBP technique is applied to shape index of 3D facial sur...
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the present work describes an improved version of MART (Multichannel ART), a neural network aimed at the adaptive recognition of multichannel patterns. As is habitual in ART networks, MART is directed at problems that...
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ISBN:
(纸本)3540660690
the present work describes an improved version of MART (Multichannel ART), a neural network aimed at the adaptive recognition of multichannel patterns. As is habitual in ART networks, MART is directed at problems that require unsupervised learning, but it has a greater level of adaptability to the characteristics of the input patterns, selectively evaluating the different signal channels on which it operates and the classes learnt, modulating the discrimination capacity, and dynamically learning / forgetting classes with regard to their level of representativity.
Extraction of some meta-information from printed documents without an OCR approach is considered. It can be statistically verified that important terms in articles are printed in italic, bold and all capital style. De...
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