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TECHNOLOGY KEYWORDS Code Description ELECTRONICS IT AND TELECOMMS Electronics

TECHNOLOGY KEYWORDS Code Description ELECTRONICS IT AND TELECOMMS Electronics

  • marina-yarberry
  • 8 Slides

separation sorting filtering 002002013 Moulding i...

MAIDS Mining Alarming Incidents from Data Streams Y

MAIDS Mining Alarming Incidents from Data Streams Y

  • faustina-dinatale
  • 2 Slides

Dora Cai David Clutter Greg Pape Jiawei Han Micha...

Guided Image Filtering Kaiming He Jian Sun and Xiaoou Tang Department of Information Engineering The Chinese University of Hong Kong Microsoft Research Asia Shenzhen Institutes of Advanced Technolog

Guided Image Filtering Kaiming He Jian Sun and Xiaoou Tang Department of Information Engineering The Chinese University of Hong Kong Microsoft Research Asia Shenzhen Institutes of Advanced Technolog

  • test
  • 14 Slides

In this paper we propose a novel type of explicit...

A Keyword Sense Disambiguation Based Approach for Noise Filtering in Twitter Motivation With growing popularity of streaming social media platforms such as Twitter for news reporting locating timely a

A Keyword Sense Disambiguation Based Approach for Noise Filtering in Twitter Motivation With growing popularity of streaming social media platforms such as Twitter for news reporting locating timely a

  • tatyana-admore
  • 1 Slides

Heravi This project has been funded by SFI grant ...

COMBINE CORNE AN EDG DETECTO Chri Harri Mik Stephen Plesse Researc Rok Manor Unite Kingdo Th Plesse Compan pic Consistency of image edge filtering is of prime importance for D interpretation of ima

COMBINE CORNE AN EDG DETECTO Chri Harri Mik Stephen Plesse Researc Rok Manor Unite Kingdo Th Plesse Compan pic Consistency of image edge filtering is of prime importance for D interpretation of ima

  • luanne-stotts
  • 6 Slides

To cater for image regions containing texture and...

Fast Bilateral Filtering for the Display of HighDynamicRange Images Fr edo Durand and Julie Dorsey Laboratory for Computer Science Massachusetts Institute of Technology Abstract We present a new techn

Fast Bilateral Filtering for the Display of HighDynamicRange Images Fr edo Durand and Julie Dorsey Laboratory for Computer Science Massachusetts Institute of Technology Abstract We present a new techn

  • liane-varnes
  • 10 Slides

It is based on a twoscale decomposition of the im...

COMBINE CORNE AN EDG DETECTO Chri Harri Mik Stephen Plesse Researc Rok Manor Unite Kingdo Th Plesse Compan pic Consistency of image edge filtering is of prime importance for D interpretation of ima

COMBINE CORNE AN EDG DETECTO Chri Harri Mik Stephen Plesse Researc Rok Manor Unite Kingdo Th Plesse Compan pic Consistency of image edge filtering is of prime importance for D interpretation of ima

  • tatiana-dople
  • 6 Slides

To cater for image regions containing texture and...

The Wisdom of the Few A Collaborative Filtering Approach Based on Expert Opinion s from the Web Xavier Amatriain Telefonica Research Via Augusta Barcelona Spain xartid

The Wisdom of the Few A Collaborative Filtering Approach Based on Expert Opinion s from the Web Xavier Amatriain Telefonica Research Via Augusta Barcelona Spain xartid

  • min-jolicoeur
  • 9 Slides

es Neal Lathia Dept of Computer Science University...

Google News Personalization Scalable Online Collaborative Filtering Abhinandan Das Google Inc

Google News Personalization Scalable Online Collaborative Filtering Abhinandan Das Google Inc

  • cheryl-pisano
  • 10 Slides

1600 Amphitheatre Pkwy Mountain View CA 94043 abh...

Restricted Boltzmann Mac hines for Collab orativ Filtering Ruslan Salakh utdino rsalakhucs

Restricted Boltzmann Mac hines for Collab orativ Filtering Ruslan Salakh utdino rsalakhucs

  • mitsue-stanley
  • 8 Slides

tor ontoedu Andriy Mnih amnihcstor ontoedu Geo5735...

Explaining Collaborative Filtering Recommendations ABSTRACT Keywords INTRODUCTION SOURCES OF ERROR ModelProcess Errors Data Errors EXPLANATIONS RESEARCH QUESTIONS What models and techniques are eff

Explaining Collaborative Filtering Recommendations ABSTRACT Keywords INTRODUCTION SOURCES OF ERROR ModelProcess Errors Data Errors EXPLANATIONS RESEARCH QUESTIONS What models and techniques are eff

  • karlyn-bohler
  • 10 Slides

25 129 2 77 519 116 3 78 509 122 4 78 497 129 5 77...

Guided Image Filtering Kaiming He Jian Sun and Xiaoou Tang Department of Information Engineering The Chinese Univer sity of Hong Kong Microsoft Research Asia Shenzhen Institutes of Advanced Technol

Guided Image Filtering Kaiming He Jian Sun and Xiaoou Tang Department of Information Engineering The Chinese Univer sity of Hong Kong Microsoft Research Asia Shenzhen Institutes of Advanced Technol

  • briana-ranney
  • 14 Slides

In this paper we propose a novel type of explicit...

Any signal processing such as amplification sampling filtering noise to a measurement

Any signal processing such as amplification sampling filtering noise to a measurement

  • marina-yarberry
  • 11 Slides

Any signal processing such as amplification sampl...

Jamie Ludwig Satellite Digital Image Analysis Portland State University Filtering Convolution Matrix Color values kernel Convolution filtering is used to modify the spatial frequency characteristic

Jamie Ludwig Satellite Digital Image Analysis Portland State University Filtering Convolution Matrix Color values kernel Convolution filtering is used to modify the spatial frequency characteristic

  • pamella-moone
  • 8 Slides

Convolution is a general purpos e filter effect f...

Video Filtering Considerations for Media Processors By David Katz and Rick Gentile Blackfin Applications Group Analog Devices Inc

Video Filtering Considerations for Media Processors By David Katz and Rick Gentile Blackfin Applications Group Analog Devices Inc

  • olivia-moreira
  • 6 Slides

Until recently designers needing to perform vi de...

Comparison of Unscented and Extended Kalman Filtering for Estimating Quaternion Motion Joseph J

Comparison of Unscented and Extended Kalman Filtering for Estimating Quaternion Motion Joseph J

  • sherrill-nordquist
  • 6 Slides

LaV iola Jr Bro wn Uni ersity echnology Center fo...

Robust Extended Kalman Filtering in Hybrid Positioning Applications Tommi PER AL A and Robert PICH Tampere University of Technology Finland Email tommi

Robust Extended Kalman Filtering in Hybrid Positioning Applications Tommi PER AL A and Robert PICH Tampere University of Technology Finland Email tommi

  • conchita-marotz
  • 9 Slides

perala robertpichetut Abstract The Kalman 64257lt...

Sparse Filtering Jiquan Ngiam Pang Wei Koh Zhenghao Chen Sonia Bhaskar Andrew Y

Sparse Filtering Jiquan Ngiam Pang Wei Koh Zhenghao Chen Sonia Bhaskar Andrew Y

  • trish-goza
  • 9 Slides

Ng Computer Science Department Stanford Universit...

Collaborative Filtering forI mplicit Feedback Datasets Yifan Hu ATT Labs Research Florham Park NJ Yehuda Koren Yahoo Research Haifa Israel Chris Volinsky ATT Labs Research Florham Park NJ Abstra

Collaborative Filtering forI mplicit Feedback Datasets Yifan Hu ATT Labs Research Florham Park NJ Yehuda Koren Yahoo Research Haifa Israel Chris Volinsky ATT Labs Research Florham Park NJ Abstra

  • alida-meadow
  • 10 Slides

These systems pas sively track different sorts of...

Automatic Track Initiation of Manoeuvring Target in Clutter Darko Mu sicki Subhash Challa Soa Suvorova Dept

Automatic Track Initiation of Manoeuvring Target in Clutter Darko Mu sicki Subhash Challa Soa Suvorova Dept

  • kittie-lecroy
  • 7 Slides

of EEE Dept of EEE EWRD University of Melbourne U...

Constant Time Weighted Median Filtering for Stereo Matching and Beyond Ziyang Ma Kaiming He Yichen Wei Jian Sun Enhua Wu University of Chinese Academy of Sciences State Key Laboratory of Computer Sc

Constant Time Weighted Median Filtering for Stereo Matching and Beyond Ziyang Ma Kaiming He Yichen Wei Jian Sun Enhua Wu University of Chinese Academy of Sciences State Key Laboratory of Computer Sc

  • min-jolicoeur
  • 8 Slides

Little attention has been seriously paid to the d...

EE Lecture Wiener Filtering In this lecture we will tak dif ferent vie of ltering

EE Lecture Wiener Filtering In this lecture we will tak dif ferent vie of ltering

  • ellena-manuel
  • 11 Slides

Pre viously we ha depended on fr equencydomain sp...

Filtering In discussing Fourier transforms we developed a number of important prop erties among them the convolution property and the modulation property

Filtering In discussing Fourier transforms we developed a number of important prop erties among them the convolution property and the modulation property

  • celsa-spraggs
  • 12 Slides

The convolution property forms the basis for the ...

Unscented Filtering and Nonlinear Estimation SIMON J

Unscented Filtering and Nonlinear Estimation SIMON J

  • test
  • 22 Slides

JULIER MEMBER IEEE AND JEFFREY K UHLMANN MEMBER...

Noise reduction by filtering is the most commonly used method for improving signaltonoise ratio

Noise reduction by filtering is the most commonly used method for improving signaltonoise ratio

  • faustina-dinatale
  • 4 Slides

The increase in settling time however can be a se...

Eigentaste A Constant Time Collaborative Filtering Algorithm Ken Goldberg and Theresa Roeder and Dhruv Gupta and Chris Perkins IEOR and EECS Departments University of California Berkeley August Abst

Eigentaste A Constant Time Collaborative Filtering Algorithm Ken Goldberg and Theresa Roeder and Dhruv Gupta and Chris Perkins IEOR and EECS Departments University of California Berkeley August Abst

  • tatiana-dople
  • 11 Slides

PCA facilitates dimensionality reduction for of64...

Adjacent Channel Interference Effects in OFDM Systems with Imperfect AntiAliasing Filtering Florian Klingler and Henrik Schulze Robert Bosch GmbH Corporate Research P

Adjacent Channel Interference Effects in OFDM Systems with Imperfect AntiAliasing Filtering Florian Klingler and Henrik Schulze Robert Bosch GmbH Corporate Research P

  • olivia-moreira
  • 5 Slides

O Box 77 77 77 D31132 Hildesheim Germany 64258oria...

Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering ZAN HUANG HSINCHUN CHEN and DANIEL ZENG The University of Arizona Recommender systems are being

Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering ZAN HUANG HSINCHUN CHEN and DANIEL ZENG The University of Arizona Recommender systems are being

  • min-jolicoeur
  • 27 Slides

Collaborative 64257ltering the most success ful r...

Internet AntiCensorship The Internet AntiCensorship IAC Division provides a variety of tools to circumvent filtering and promote Internet freedom for citizens of countries whose governments censor th

Internet AntiCensorship The Internet AntiCensorship IAC Division provides a variety of tools to circumvent filtering and promote Internet freedom for citizens of countries whose governments censor th

  • giovanna-bartolotta
  • 2 Slides

The Division works closely with BBG broadcasters ...

An Introduction to WSRD Clutter Suppression and Some Tips for Effective Suppression Ut ilization Stephen M

An Introduction to WSRD Clutter Suppression and Some Tips for Effective Suppression Ut ilization Stephen M

  • test
  • 16 Slides

Goss NEXRAD Weather Service Office Nashv ille TN ...

Object Recognition in Dense Clutter MAR J

Object Recognition in Dense Clutter MAR J

  • calandra-battersby
  • 17 Slides

BRA VO HANY ARID Observers in recognition experim...

OCCLUSION CLUTTER AND ILLUMINATION INVARIANT OBJECT RECOGNITION Carsten Steger MVTecSoftwareGmbH Neherstrae M unchen Germany stegermvtec

OCCLUSION CLUTTER AND ILLUMINATION INVARIANT OBJECT RECOGNITION Carsten Steger MVTecSoftwareGmbH Neherstrae M unchen Germany stegermvtec

  • ellena-manuel
  • 6 Slides

com Commission III Working Group III5 KEY WORDS Co...

Clutter Reduction in MultiDimensional Data Visualization Using Dimension Reordering Wei Peng Matthew O

Clutter Reduction in MultiDimensional Data Visualization Using Dimension Reordering Wei Peng Matthew O

  • karlyn-bohler
  • 8 Slides

Ward and Elke A Rundensteiner Computer Science De...

Explaining Collaborative Filtering Recommendations ABSTRACT Keywords INTRODUCTION SOURCES OF ERROR ModelProcess Errors Data Errors EXPLANATIONS RESEARCH QUESTIONS What models and techniques are eff

Explaining Collaborative Filtering Recommendations ABSTRACT Keywords INTRODUCTION SOURCES OF ERROR ModelProcess Errors Data Errors EXPLANATIONS RESEARCH QUESTIONS What models and techniques are eff

  • lindy-dunigan
  • 10 Slides

25 129 2 77 519 116 3 78 509 122 4 78 497 129 5 77...

Dont Agonize Organize Your Office Now Simple Solutions

Dont Agonize Organize Your Office Now Simple Solutions

  • briana-ranney
  • 4 Slides

clutter Dont Agonize Organize Miller Organizing...

RealTime Bilateral Filtering Qingxiong Yang KarHan Ta

RealTime Bilateral Filtering Qingxiong Yang KarHan Ta

  • liane-varnes
  • 8 Slides

aiuiucedu qyang6 Abstract We propose a new bilater...

Cut the clutter and drop the pounds in just weeks PET

Cut the clutter and drop the pounds in just weeks PET

  • tawny-fly
  • 1 Slides

He has motivated thousands around the globe to li...

Discrete Mathematics NorthHolland Clutters and mat

Discrete Mathematics NorthHolland Clutters and mat

  • faustina-dinatale
  • 11 Slides

MA F I N I C Av Prof Gama Pinto 2 1699 Lisboa Cod...

Statistical Enroute Filtering of Injected False Data i

Statistical Enroute Filtering of Injected False Data i

  • lindy-dunigan
  • 12 Slides

uclaedu Abstract In a largescale sensor network i...

User-Centric Evaluation of a K-Furthest NeighborCollaborative Filtering Recommender Algorithm

User-Centric Evaluation of a K-Furthest NeighborCollaborative Filtering Recommender Algorithm

  • trish-goza
  • 10 Slides

rformeasuringthelevelofdiversityarec-ommendational...

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