Conferences related to Dictionaries

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2016 IEEE International Conference on Image Processing (ICIP)

Signal processing, image processing, biomedical imaging, multimedia, video, multidemensional.


2012 21st International Conference on Pattern Recognition (ICPR)

ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.


2009 2nd International Symposium on Knowledge Acquisition and Modeling (KAM)

2009 The 2nd International Symposium on Knowledge Acquisition and Modeling (KAM 2009) is concerned with the aspects of Intelligent Information Processing, acquiring, modeling, managing and exploiting knowledge, and the role of these aspects in the construction of knowledge-intensive systems and Intelligent Information services.


2007 22nd International Symposium on Computer and Information Sciences (ISCIS)

The 22nd ISCIS conference will accept research papers in one of the following three tracks: Computer Vision, Graphics and Intelligence - Networks and Systems - Data Management.



Periodicals related to Dictionaries

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Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...


Signal Processing Letters, IEEE

Rapid dissemination of new results in signal processing world-wide.


Signal Processing, IEEE Transactions on

The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.




Xplore Articles related to Dictionaries

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Speech and text query based Tamil - English Cross Language Information Retrieval system

P. Iswarya; V. Radha 2014 International Conference on Computer Communication and Informatics, 2014

The number of Web Users accessing the Internet becomes increasing day by day. Any kind of required information can be obtained anytime by anybody from the web. Information retrieval is the fact that there is vast amount of garbage that surrounds any useful information. Such information should be easily accessible and digestible. Internet is no longer monolingual and non-English content ...


A New Anti-Forensic Tool Based on a Simple Data Encryption Scheme

Sang Su Lee; Ku-Young Chang; Deokgyu Lee; Dowon Hong Future Generation Communication and Networking (FGCN 2007), 2007

In this paper, we discuss a simple encryption scheme in which a secret file is encrypted twice: one by a common encryption algorithm like AES and another by XOR. Despite the first key is revealed by guessing or dictionary-based attack, the attacker can not reconstruct the original secret until knows the files used to derive the second key block according ...


Low dose CT image reconstruction with learned sparsifying transform

Xuehang Zheng; Zening Lu; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessier 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt ...


Analysis versus synthesis in signal priors

Michael Elad; Peyman Milanfar; Ron Rubinstein 2006 14th European Signal Processing Conference, 2006

The concept of prior probability for signals plays a key role in the successful solution of many inverse problems. Much of the literature on this topic can be divided between analysis-based and synthesis-based priors. Analysis-based priors assign probability to a signal through various forward measurements of it, while synthesis-based priors seek a reconstruction of the signal as a combination of ...


Structuring information from natural language descriptions: Accounting for uncertainty

Julia M. Taylor; Victor Raskin 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2012

In this paper, we report on a part of a large experiment in structuring information from natural language descriptions of animals from a children's dictionary. The structuring included the recognition and postulation of properties and capturing the is-a hierarchy from the descriptions. The material was taken from the 2007 edition of the American Heritage First Dictionary. We applied the methodology ...


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Educational Resources on Dictionaries

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eLearning

Speech and text query based Tamil - English Cross Language Information Retrieval system

P. Iswarya; V. Radha 2014 International Conference on Computer Communication and Informatics, 2014

The number of Web Users accessing the Internet becomes increasing day by day. Any kind of required information can be obtained anytime by anybody from the web. Information retrieval is the fact that there is vast amount of garbage that surrounds any useful information. Such information should be easily accessible and digestible. Internet is no longer monolingual and non-English content ...


A New Anti-Forensic Tool Based on a Simple Data Encryption Scheme

Sang Su Lee; Ku-Young Chang; Deokgyu Lee; Dowon Hong Future Generation Communication and Networking (FGCN 2007), 2007

In this paper, we discuss a simple encryption scheme in which a secret file is encrypted twice: one by a common encryption algorithm like AES and another by XOR. Despite the first key is revealed by guessing or dictionary-based attack, the attacker can not reconstruct the original secret until knows the files used to derive the second key block according ...


Low dose CT image reconstruction with learned sparsifying transform

Xuehang Zheng; Zening Lu; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessier 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt ...


Analysis versus synthesis in signal priors

Michael Elad; Peyman Milanfar; Ron Rubinstein 2006 14th European Signal Processing Conference, 2006

The concept of prior probability for signals plays a key role in the successful solution of many inverse problems. Much of the literature on this topic can be divided between analysis-based and synthesis-based priors. Analysis-based priors assign probability to a signal through various forward measurements of it, while synthesis-based priors seek a reconstruction of the signal as a combination of ...


Structuring information from natural language descriptions: Accounting for uncertainty

Julia M. Taylor; Victor Raskin 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2012

In this paper, we report on a part of a large experiment in structuring information from natural language descriptions of animals from a children's dictionary. The structuring included the recognition and postulation of properties and capturing the is-a hierarchy from the descriptions. The material was taken from the 2007 edition of the American Heritage First Dictionary. We applied the methodology ...


More eLearning Resources

IEEE.tv Videos

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IEEE-USA E-Books

  • No title

    Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing ov rcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the spa se coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.

  • Primitives in Meaning Definition

    This chapter contains sections titled: Escape Arguments, Putnam's Argument, Charniak's Argument, Toward a Clearer View of Semantic Primitives

  • Wordbooks as Human Artifacts: Dictionaries and Thesauri

    This chapter contains sections titled: The Standard Dictionary, The Classic Thesaurus, Bilingual Dictionaries, Style Dictionaries, Concordances, Are There Psychological Constraints on Dictionaries?, Are Word Senses Real?, Kay's Mental Lexicon vs. Concrete Dictionaries, Further Problems with Dictionaries

  • English-Japanese Machine Translation

    This chapter contains sections titled: Introduction, Dictionaries and Tables, Translation Principles and Flow Diagram, Machine Organization, Conclusion, References

  • Early Computational Approaches: Tasks and Tools

    This chapter contains sections titled: Computing over Whole Dictionaries: Olney and Revard at SDC, Amsler's Thesis, Michiel's Early Work on LDOCE, Lattices and Thesauri, Sparck Jones's Thesis and the Transition to Information Retrieval, Information Retrieval and Thesauri, Up to Modern Times: Wilks on Thesauri and Frames

  • References

    The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how Machine Learning techniques can improve Statistical Machine Translation, currently at the forefront of research in the field. The book looks first at enabling technologies-- technologies that solve problems that are not Machine Translation proper but are linked closely to the development of a Machine Translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.ContributorsSrinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob Elming, George Foster, Jesús Giménez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mahé, Lluís Màrquez, Evgeny Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian, Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada

  • Text Analysis and Its Relationship to Dictionaries: Dictionaries as Texts

    This chapter contains sections titled: The Dictionary as a Text: LDOCE and COBUILD, Dictionaries as Texts, Dictionaries as Knowledge Structures, Text Analysis on a Large Scale, Walker and Amsler, Pathtrieve, Pathfinder Networks

  • Query Translation Using Evolutionary Programming for Multi-Lingual Information Retrieval

    Multi-lingual information retrieval (IR) systems apply queries in one language to a document collection in several different languages with the goal of retrieving only those documents relevant to the query. At first glance, deep linguistic analysis and translation of the query appears necessary before retrievals can be performed. IR systems are unique in natural language processing, however, because a pattern of term occurrences in a document generally suffices to determine the subject matter; word order is largely irrelevant. Translated queries are therefore primarily derived by a mapping from a word set in the query language to a word set in the language of the derived query. Large parallel text collections with sentencelevel alignments can provide a baseline for evaluating the correctness of a query translation, but the determination of members of the query translation remains problematic. Constructing a query from machine-readable, bilingual dictionaries and assigning term weights by the evolutionary optimization of a population of potential weighting schemes presents a solution to the difficulties of generating translated queries. In this approach, differences in the rank statistics on the comparative recall results for a query against its native language and its translation against its native language determine the fitness of a tentative query translation.

  • Contributors

    The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how Machine Learning techniques can improve Statistical Machine Translation, currently at the forefront of research in the field. The book looks first at enabling technologies-- technologies that solve problems that are not Machine Translation proper but are linked closely to the development of a Machine Translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.ContributorsSrinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob Elming, George Foster, Jesús Giménez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mahé, Lluís Màrquez, Evgeny Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian, Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada

  • The Construction of Modern Lexicons

    This chapter contains sections titled: Lexicons vs. Wordbooks, AI and Linguistic Principles of Lexicon (Re)construction, AI and Psychological Principles of Lexicon (Re)construction, Lexical Acquisition from Human Subjects, Lexical Acquisition from Texts, Lexical Workbenches: Carnegie Mellon's ONTOS and MCC's LUKE, Extended-Aspect Calculus, The "Neutral Lexicon"?



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