3D-Assisted Image Feature Synthesis for Novel Views of an Object
|
|
- Mabel Arnold
- 5 years ago
- Views:
Transcription
1 3D-Assisted Image Feature Synthesis for Novel Views of an Object Hao Su* Fan Wang* Li Yi Leonidas Guibas * Equal contribution
2 View-agnostic Image Retrieval Retrieval using AlexNet features Query
3 Cross-view Image Comparison
4 Cross-view Image Comparison The comparison is between the underlying 3D objects
5 Reconstruct 3D and then compare? Su et al, SIGGRAPH 14 Kar et al, CVPR 15 Huang et al, SIGGRAPH 15
6 Single-image based 3D Reconstruction is hard Common dependencies: Many dependencies Not Robust Fg/bg segmentation Slow Keypoint detection 2D image part segmentation 3D shape part segmentation 2D-3D Correspondence Non-convex iterative optimization
7 Our Formulation: Novel View Feature Synthesis Observed view (HoG feature as an example)
8 Our Novel View Feature Synthesis Results (HoG feature as an example)
9 Outline Motivation Approach Applications Method Diagnosis Conclusion
10 Key idea Learn from a dataset of many objects with multi-view features
11 Key idea Learn from a dataset of multi-view features The dataset is generated by rendering 3D models d
12 Key idea Learn from a dataset of multi-view features The dataset is generated by rendering large-scale 3D models
13 3D-assisted Feature Synthesis: Nearest Neighbour Observed view image Novel view feature (HoG feature as an example)
14 3D-assisted Feature Synthesis: Nearest Neighbour Observed view image Strong assumption: very similar model exists Novel view feature (HoG feature as an example)
15 3D-assisted Feature Synthesis: Multiple Shapes Observed view image... Novel view feature (HoG feature as an example)
16 3D-assisted Feature Synthesis: Multiple Shapes Attention: Brain games start!
17 Pipeline Observed view image Novel view feature (HoG feature as an example)
18 Pipeline Observed view image Novel view feature (HoG feature as an example)
19 Pipeline Observed view image Novel view feature (HoG feature as an example)
20 Pipeline Observed view image + + Novel view feature (HoG feature as an example)
21 Pipeline Observed view image + + Novel view feature (HoG feature as an example)
22 Pipeline Observed view image Locally Linear Reconstruction Novel view feature (HoG feature as an example)
23 Pipeline Observed view image Locally Linear Reconstruction Novel view feature (HoG feature as an example)
24 Pipeline Observed view image Locally Linear Reconstruction Novel view feature (HoG feature as an example)
25 Pipeline Observed view image Locally Linear Reconstruction Novel view feature Inter-shape relationship (HoG feature as an example)
26 Surrogate Relationship Discovery Observed view image Locally Linear Reconstruction ? + + Novel view feature Inter-shape relationship (HoG feature as an example)
27 Surrogate Relationship Discovery Observed view Shape Collection Novel view
28 Surrogate Relationship Discovery Observed view Shape Collection Novel view Surrogate suitability matrix
29 Formal Definition of Surrogate Suitability Shape Collection Observed view Assume A, B are discrete random variables A Novel view B
30 Formal Definition of Surrogate Suitability Shape Collection Observed view Assume A, B are discrete random variables (a 1, b 1 ), (a 2, b 2 ), are i.i.d samples of (A, B) A Novel view e.g. a 1 a 2 b 1 b 2 B
31 Formal Definition of Surrogate Suitability Shape Collection Observed view Assume A, B are discrete random variables (a 1, b 1 ), (a 2, b 2 ), are i.i.d samples of (A, B) A Novel view e.g. a 1 a 2 Surrogate suitability: b 1 b 2 B γ A; B = log P(b 1 = b 2 a 1 = a 2 )
32 Formal Definition of Surrogate Suitability Shape Collection Observed view Assume A, B are discrete random variables (a 1, b 1 ), (a 2, b 2 ), are i.i.d samples of (A, B) How well can the sameness at A predict the sameness at B? A Novel view e.g. a 1 a 2 Surrogate suitability: b 1 b 2 B γ A; B = log P(b 1 = b 2 a 1 = a 2 )
33 Formal Definition of Surrogate Suitability Shape Collection Observed view Assume A, B are discrete random variables (a 1, b 1 ), (a 2, b 2 ), are i.i.d samples of (A, B) How well can the sameness at A predict the sameness at B? A Novel view e.g. a 1 a 2 Cross-view transfer of relationships B b 1 b 2 Surrogate suitability: γ A; B = log P(b 1 = b 2 a 1 = a 2 )
34 Estimation of Surrogate Suitability Derivation shows H R : Renyi-entropy
35 Estimation of Surrogate Suitability Derivation shows Sample complexity: tight bound Θ V A + V B where V A and V B are vocabulary size of A and B
36 Estimation of Surrogate Suitability Derivation shows Sample complexity: tight bound Θ V A + V B where V A and V B are vocabulary size of A and B Theoretically optimal algorithm is proposed that reaches the bound
37 Estimation of Surrogate Suitability Derivation shows Sample complexity: tight bound Θ V A + V B where V A and V B are vocabulary size of A and B Theoretically optimal algorithm is proposed that reaches the bound Strong connection with Mutual Information
38 More Visualization of Surrogate Suitability Matrix Novel view Observed view B
39 More Visualization of Surrogate Suitability Matrix Novel view Observed view B
40 More Visualization of Surrogate Suitability Matrix Novel view Observed view B
41 Review of Pipeline Observed view image Novel view feature
42 Inter-shape relationship Review of Pipeline Observed view image + Inter-shape relationship: Knowledge transfer from 3D shape database to+ new instance + Novel view feature
43 Intra-shape relationship Inter-shape relationship Review of Pipeline Observed view image Intra-shape relationship: + Inter-shape relationship: Knowledge transfer from observed view to novel view Knowledge transfer from 3D shape database to+ new instance + Novel view feature
44 Outline Motivation Approach Applications Method Diagnosis Conclusion
45 Application: Cross-view localized image comparison
46 Cross-view Image Retrieval
47 Application: View-agnostic Image Retrieval HoG L2 vertical bars swivel base Ours (combined HoG)
48 Application: View-agnostic Image Retrieval HoG L2 vertical bars swivel base Ours (combined HoG)
49 Application: View-agnostic Image Retrieval HoG L2 vertical bars swivel base Ours (combined HoG)
50 Part-based View-agnostic Image Retrieval
51 Generalizability to Many Feature Types Task: fine-grained retrieval (images and annotations are from ImageNet) Metric: Average Precision
52 Outline Motivation Approach Applications Method Diagnosis Conclusion
53 How many shapes are sufficient? 200 (Measured by Average Precision on Fine-grained retrieval for Chairs)
54 How many neighboring shapes for interpolation? 80 (Measured by Average Precision on Fine-grained retrieval for Chairs)
55 How well can one view predict another view? Controlled diagnosis on renderings Cross-view retrieval rank
56 Outline Motivation Approach Applications Method Diagnosis Conclusion
57 Conclusion A novel framework for synthesizing object features at novel views 3D shape database provides the knowledge of feature synthesis For relationship transfer, surrogate suitability is defined, which is a type of predictability between random variables. A theoretically optimal estimator is proposed
58 Thank you!
59
60
Graph-of-word and TW-IDF: New Approach to Ad Hoc IR (CIKM 2013) Learning to Rank: From Pairwise Approach to Listwise Approach (ICML 2007)
Graph-of-word and TW-IDF: New Approach to Ad Hoc IR (CIKM 2013) Learning to Rank: From Pairwise Approach to Listwise Approach (ICML 2007) Qin Huazheng 2014/10/15 Graph-of-word and TW-IDF: New Approach
More informationRecent Advances in Sampling-based Alpha Matting
Recent Advances in Sampling-based Alpha Matting Presented By: Ahmad Al-Kabbany Under the Supervision of: Prof.Eric Dubois Recent Advances in Sampling-based Alpha Matting Presented By: Ahmad Al-Kabbany
More informationHash Function Learning via Codewords
Hash Function Learning via Codewords 2015 ECML/PKDD, Porto, Portugal, September 7 11, 2015. Yinjie Huang 1 Michael Georgiopoulos 1 Georgios C. Anagnostopoulos 2 1 Machine Learning Laboratory, University
More information1 st Keypoints Challenge. ImageNet and COCO Visual Recognition Challenges Workshop. Yin Cui, Tsung-Yi Lin, Matteo Ruggero Ronchi, Genevieve Patterson
1 st Keypoints Challenge Yin Cui, Tsung-Yi Lin, Matteo Ruggero Ronchi, Genevieve Patterson ImageNet and COCO Visual Recognition Challenges Workshop Sunday, October 9th, ECCV 2016 Dataset Dataset Statistics
More informationSketchNet: Sketch Classification with Web Images[CVPR `16]
SketchNet: Sketch Classification with Web Images[CVPR `16] CS688 Paper Presentation 1 Doheon Lee 20183398 2018. 10. 23 Table of Contents Introduction Background SketchNet Result 2 Introduction Properties
More informationWavelet-based image compression
Institut Mines-Telecom Wavelet-based image compression Marco Cagnazzo Multimedia Compression Outline Introduction Discrete wavelet transform and multiresolution analysis Filter banks and DWT Multiresolution
More informationAutocomplete Sketch Tool
Autocomplete Sketch Tool Sam Seifert, Georgia Institute of Technology Advanced Computer Vision Spring 2016 I. ABSTRACT This work details an application that can be used for sketch auto-completion. Sketch
More informationSemantic Localization of Indoor Places. Lukas Kuster
Semantic Localization of Indoor Places Lukas Kuster Motivation GPS for localization [7] 2 Motivation Indoor navigation [8] 3 Motivation Crowd sensing [9] 4 Motivation Targeted Advertisement [10] 5 Motivation
More informationA Novel Method for Enhancing Satellite & Land Survey Images Using Color Filter Array Interpolation Technique (CFA)
A Novel Method for Enhancing Satellite & Land Survey Images Using Color Filter Array Interpolation Technique (CFA) Suma Chappidi 1, Sandeep Kumar Mekapothula 2 1 PG Scholar, Department of ECE, RISE Krishna
More informationTRANSFORMING PHOTOS TO COMICS USING CONVOLUTIONAL NEURAL NETWORKS. Tsinghua University, China Cardiff University, UK
TRANSFORMING PHOTOS TO COMICS USING CONVOUTIONA NEURA NETWORKS Yang Chen Yu-Kun ai Yong-Jin iu Tsinghua University, China Cardiff University, UK ABSTRACT In this paper, inspired by Gatys s recent work,
More informationComputer Graphics (Fall 2011) Outline. CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi
Computer Graphics (Fall 2011) CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi Some slides courtesy Thomas Funkhouser and Pat Hanrahan Adapted version of CS 283 lecture http://inst.eecs.berkeley.edu/~cs283/fa10
More informationVideo Object Segmentation with Re-identification
Video Object Segmentation with Re-identification Xiaoxiao Li, Yuankai Qi, Zhe Wang, Kai Chen, Ziwei Liu, Jianping Shi Ping Luo, Chen Change Loy, Xiaoou Tang The Chinese University of Hong Kong, SenseTime
More informationOptimizing Media Access Strategy for Competing Cognitive Radio Networks Y. Gwon, S. Dastangoo, H. T. Kung
Optimizing Media Access Strategy for Competing Cognitive Radio Networks Y. Gwon, S. Dastangoo, H. T. Kung December 12, 2013 Presented at IEEE GLOBECOM 2013, Atlanta, GA Outline Introduction Competing Cognitive
More informationDynamic Data-Driven Adaptive Sampling and Monitoring of Big Spatial-Temporal Data Streams for Real-Time Solar Flare Detection
Dynamic Data-Driven Adaptive Sampling and Monitoring of Big Spatial-Temporal Data Streams for Real-Time Solar Flare Detection Dr. Kaibo Liu Department of Industrial and Systems Engineering University of
More informationData and Knowledge as Infrastructure. Chaitan Baru Senior Advisor for Data Science CISE Directorate National Science Foundation
Data and Knowledge as Infrastructure Chaitan Baru Senior Advisor for Data Science CISE Directorate National Science Foundation 1 Motivation Easy access to data The Hello World problem (courtesy: R.V. Guha)
More informationOptimization Techniques for Alphabet-Constrained Signal Design
Optimization Techniques for Alphabet-Constrained Signal Design Mojtaba Soltanalian Department of Electrical Engineering California Institute of Technology Stanford EE- ISL Mar. 2015 Optimization Techniques
More informationENERGY-EFFICIENT ALGORITHMS FOR SENSOR NETWORKS
ENERGY-EFFICIENT ALGORITHMS FOR SENSOR NETWORKS Prepared for: DARPA Prepared by: Krishnan Eswaran, Engineer Cornell University May 12, 2003 ENGRC 350 RESEARCH GROUP 2003 Krishnan Eswaran Energy-Efficient
More informationMIMO Radar and Communication Spectrum Sharing with Clutter Mitigation
MIMO Radar and Communication Spectrum Sharing with Clutter Mitigation Bo Li and Athina Petropulu Department of Electrical and Computer Engineering Rutgers, The State University of New Jersey Work supported
More informationSteganalysis in resized images
Steganalysis in resized images Jan Kodovský, Jessica Fridrich ICASSP 2013 1 / 13 Outline 1. Steganography basic concepts 2. Why we study steganalysis in resized images 3. Eye-opening experiment on BOSSbase
More informationOFDM Pilot Optimization for the Communication and Localization Trade Off
SPCOMNAV Communications and Navigation OFDM Pilot Optimization for the Communication and Localization Trade Off A. Lee Swindlehurst Dept. of Electrical Engineering and Computer Science The Henry Samueli
More informationStructure and Synthesis of Robot Motion
Structure and Synthesis of Robot Motion Motion Synthesis in Groups and Formations I Subramanian Ramamoorthy School of Informatics 5 March 2012 Consider Motion Problems with Many Agents How should we model
More informationRomantic Partnerships and the Dispersion of Social Ties
Introduction Embeddedness and Evaluation Combining Features Romantic Partnerships and the of Social Ties Lars Backstrom Jon Kleinberg presented by Yehonatan Cohen 2014-11-12 Introduction Embeddedness and
More informationEE 123 Discussion Section 6. Frank Ong March 14th, 2016
EE 123 Discussion Section 6 Frank Ong March 14th, 2016 Plan Sparse FFT Magnitude Filter Design with convex optimization Sparse FFT Given a length-n signal, FFT takes O(N log N) time to compute its DFT
More informationSuper resolution with Epitomes
Super resolution with Epitomes Aaron Brown University of Wisconsin Madison, WI Abstract Techniques exist for aligning and stitching photos of a scene and for interpolating image data to generate higher
More informationSketch-a-Net that Beats Humans
Sketch-a-Net that Beats Humans Qian Yu SketchLab@QMUL Queen Mary University of London 1 Authors Qian Yu Yongxin Yang Yi-Zhe Song Tao Xiang Timothy Hospedales 2 Let s play a game! Round 1 Easy fish face
More informationReal Time Word to Picture Translation for Chinese Restaurant Menus
Real Time Word to Picture Translation for Chinese Restaurant Menus Michelle Jin, Ling Xiao Wang, Boyang Zhang Email: mzjin12, lx2wang, boyangz @stanford.edu EE268 Project Report, Spring 2014 Abstract--We
More informationFast Online Learning of Antijamming and Jamming Strategies
Fast Online Learning of Antijamming and Jamming Strategies Y. Gwon, S. Dastangoo, C. Fossa, H. T. Kung December 9, 2015 Presented at the 58 th IEEE Global Communications Conference, San Diego, CA This
More informationColorful Image Colorizations Supplementary Material
Colorful Image Colorizations Supplementary Material Richard Zhang, Phillip Isola, Alexei A. Efros {rich.zhang, isola, efros}@eecs.berkeley.edu University of California, Berkeley 1 Overview This document
More informationRecent Advances in Image Deblurring. Seungyong Lee (Collaboration w/ Sunghyun Cho)
Recent Advances in Image Deblurring Seungyong Lee (Collaboration w/ Sunghyun Cho) Disclaimer Many images and figures in this course note have been copied from the papers and presentation materials of previous
More informationWildlife Census via LSH-based animal tracking APOORV PATWARDHAN
1 Wildlife Census via LSH-based animal tracking APOORV PATWARDHAN National Parks and wildlife conservation 2 Jim Corbett National Park, India Amboseli National Park, Kenya And many more The Challenge 3
More informationVision Defect Identification System (VDIS) using Knowledge Base and Image Processing Framework
Vishal Dahiya* et al. / (IJRCCT) INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER AND COMMUNICATION TECHNOLOGY Vol No. 1, Issue No. 1 Vision Defect Identification System (VDIS) using Knowledge Base and Image
More informationUsing the Time Dimension to Sense Signals with Partial Spectral Overlap. Mihir Laghate and Danijela Cabric 5 th December 2016
Using the Time Dimension to Sense Signals with Partial Spectral Overlap Mihir Laghate and Danijela Cabric 5 th December 2016 Outline Goal, Motivation, and Existing Work System Model Assumptions Time-Frequency
More informationDistributed Collaborative Path Planning in Sensor Networks with Multiple Mobile Sensor Nodes
7th Mediterranean Conference on Control & Automation Makedonia Palace, Thessaloniki, Greece June 4-6, 009 Distributed Collaborative Path Planning in Sensor Networks with Multiple Mobile Sensor Nodes Theofanis
More informationDYNAMIC CONVOLUTIONAL NEURAL NETWORK FOR IMAGE SUPER- RESOLUTION
Journal of Advanced College of Engineering and Management, Vol. 3, 2017 DYNAMIC CONVOLUTIONAL NEURAL NETWORK FOR IMAGE SUPER- RESOLUTION Anil Bhujel 1, Dibakar Raj Pant 2 1 Ministry of Information and
More informationDeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. ECE 289G: Paper Presentation #3 Philipp Gysel
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition ECE 289G: Paper Presentation #3 Philipp Gysel Autonomous Car ECE 289G Paper Presentation, Philipp Gysel Slide 2 Source: maps.google.com
More informationSpatial Color Indexing using ACC Algorithm
Spatial Color Indexing using ACC Algorithm Anucha Tungkasthan aimdala@hotmail.com Sarayut Intarasema Darkman502@hotmail.com Wichian Premchaiswadi wichian@siam.edu Abstract This paper presents a fast and
More informationConnected Identifying Codes
Connected Identifying Codes Niloofar Fazlollahi, David Starobinski and Ari Trachtenberg Dept. of Electrical and Computer Engineering Boston University, Boston, MA 02215 Email: {nfazl,staro,trachten}@bu.edu
More informationDetection of Compound Structures in Very High Spatial Resolution Images
Detection of Compound Structures in Very High Spatial Resolution Images Selim Aksoy Department of Computer Engineering Bilkent University Bilkent, 06800, Ankara, Turkey saksoy@cs.bilkent.edu.tr Joint work
More informationThe Capability of Error Correction for Burst-noise Channels Using Error Estimating Code
The Capability of Error Correction for Burst-noise Channels Using Error Estimating Code Yaoyu Wang Nanjing University yaoyu.wang.nju@gmail.com June 10, 2016 Yaoyu Wang (NJU) Error correction with EEC June
More informationConvolutional Networks for Image Segmentation: U-Net 1, DeconvNet 2, and SegNet 3
Convolutional Networks for Image Segmentation: U-Net 1, DeconvNet 2, and SegNet 3 1 Olaf Ronneberger, Philipp Fischer, Thomas Brox (Freiburg, Germany) 2 Hyeonwoo Noh, Seunghoon Hong, Bohyung Han (POSTECH,
More informationThe Visual Language of New Media the Book as Database
The Visual Language of New Media the Book as Database Wild Card Symposium 31.10.2012 Katía Truijen Eva Valkhoff Serena Westra Sasha Wood >>1
More informationA TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION. Scott Deeann Chen and Pierre Moulin
A TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION Scott Deeann Chen and Pierre Moulin University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering 5 North Mathews
More informationConstructing local discriminative features for signal classification
Constructing local discriminative features for signal classification Local features for signal classification Outline Motivations Problem formulation Lifting scheme Local features Conclusions Toy example
More informationNTU CSIE. Advisor: Wu Ja Ling, Ph.D.
An Interactive Background Blurring Mechanism and Its Applications NTU CSIE Yan Chih Yu Advisor: Wu Ja Ling, Ph.D. 1 2 Outline Introduction Related Work Method Object Segmentation Depth Map Generation Image
More informationAutomatic feature-queried bird identification system based on entropy and fuzzy similarity
Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications 34 (2008) 2879 2884 www.elsevier.com/locate/eswa Automatic feature-queried bird identification
More informationSpline wavelet based blind image recovery
Spline wavelet based blind image recovery Ji, Hui ( 纪辉 ) National University of Singapore Workshop on Spline Approximation and its Applications on Carl de Boor's 80 th Birthday, NUS, 06-Nov-2017 Spline
More informationTravel Photo Album Summarization based on Aesthetic quality, Interestingness, and Memorableness
Travel Photo Album Summarization based on Aesthetic quality, Interestingness, and Memorableness Jun-Hyuk Kim and Jong-Seok Lee School of Integrated Technology and Yonsei Institute of Convergence Technology
More informationThe Game-Theoretic Approach to Machine Learning and Adaptation
The Game-Theoretic Approach to Machine Learning and Adaptation Nicolò Cesa-Bianchi Università degli Studi di Milano Nicolò Cesa-Bianchi (Univ. di Milano) Game-Theoretic Approach 1 / 25 Machine Learning
More informationA. Siffer, P-A Fouque, A. Termier and C. Largouet April 26, 2017
A. Siffer, P-A Fouque, A. Termier and C. Largouet April 26, 2017 Context Providing better thresholds Finding anomalies in streams Application to intrusion detection A more general framework 1 Massive
More informationGames, Privacy and Distributed Inference for the Smart Grid
CUHK September 17, 2013 Games, Privacy and Distributed Inference for the Smart Grid Vince Poor (poor@princeton.edu) Supported in part by NSF Grant CCF-1016671 and in part by the Marie Curie Outgoing Fellowship
More informationSimple, Optimal, Fast, and Robust Wireless Random Medium Access Control
Simple, Optimal, Fast, and Robust Wireless Random Medium Access Control Jianwei Huang Department of Information Engineering The Chinese University of Hong Kong KAIST-CUHK Workshop July 2009 J. Huang (CUHK)
More informationCommunication Theory II
Communication Theory II Lecture 13: Information Theory (cont d) Ahmed Elnakib, PhD Assistant Professor, Mansoura University, Egypt March 22 th, 2015 1 o Source Code Generation Lecture Outlines Source Coding
More informationChapter 2 Distributed Consensus Estimation of Wireless Sensor Networks
Chapter 2 Distributed Consensus Estimation of Wireless Sensor Networks Recently, consensus based distributed estimation has attracted considerable attention from various fields to estimate deterministic
More informationModeling and Synthesis of Aperture Effects in Cameras
Modeling and Synthesis of Aperture Effects in Cameras Douglas Lanman, Ramesh Raskar, and Gabriel Taubin Computational Aesthetics 2008 20 June, 2008 1 Outline Introduction and Related Work Modeling Vignetting
More informationMatching Words and Pictures
Matching Words and Pictures Dan Harvey & Sean Moran 27th Feburary 2009 Dan Harvey & Sean Moran (DME) Matching Words and Pictures 27th Feburary 2009 1 / 40 1 Introduction 2 Preprocessing Segmentation Feature
More informationA New Control Theory for Dynamic Data Driven Systems
A New Control Theory for Dynamic Data Driven Systems Nikolai Matni Computing and Mathematical Sciences Joint work with Yuh-Shyang Wang, James Anderson & John C. Doyle New application areas 1 New application
More informationLecture 7: Scene Text Detection and Recognition. Dr. Cong Yao Megvii (Face++) Researcher
Lecture 7: Scene Text Detection and Recognition Dr. Cong Yao Megvii (Face++) Researcher yaocong@megvii.com Outline Background and Introduction Conventional Methods Deep Learning Methods Datasets and Competitions
More informationComparative Study of Different Wavelet Based Interpolation Techniques
Comparative Study of Different Wavelet Based Interpolation Techniques 1Computer Science Department, Centre of Computer Science and Technology, Punjabi University Patiala. 2Computer Science Department,
More informationComparing Computer-predicted Fixations to Human Gaze
Comparing Computer-predicted Fixations to Human Gaze Yanxiang Wu School of Computing Clemson University yanxiaw@clemson.edu Andrew T Duchowski School of Computing Clemson University andrewd@cs.clemson.edu
More informationCSE548, AMS542: Analysis of Algorithms, Fall 2016 Date: Sep 25. Homework #1. ( Due: Oct 10 ) Figure 1: The laser game.
CSE548, AMS542: Analysis of Algorithms, Fall 2016 Date: Sep 25 Homework #1 ( Due: Oct 10 ) Figure 1: The laser game. Task 1. [ 60 Points ] Laser Game Consider the following game played on an n n board,
More informationWireless communications: from simple stochastic geometry models to practice III Capacity
Wireless communications: from simple stochastic geometry models to practice III Capacity B. Błaszczyszyn Inria/ENS Workshop on Probabilistic Methods in Telecommunication WIAS Berlin, November 14 16, 2016
More informationOn Coding for Cooperative Data Exchange
On Coding for Cooperative Data Exchange Salim El Rouayheb Texas A&M University Email: rouayheb@tamu.edu Alex Sprintson Texas A&M University Email: spalex@tamu.edu Parastoo Sadeghi Australian National University
More informationEarly art: events. Baroque art: portraits. Renaissance art: events. Being There: Capturing and Experiencing a Sense of Place
Being There: Capturing and Experiencing a Sense of Place Early art: events Richard Szeliski Microsoft Research Symposium on Computational Photography and Video Lascaux Early art: events Early art: events
More informationRetrieval of Large Scale Images and Camera Identification via Random Projections
Retrieval of Large Scale Images and Camera Identification via Random Projections Renuka S. Deshpande ME Student, Department of Computer Science Engineering, G H Raisoni Institute of Engineering and Management
More informationSynthesizing Interpretable Strategies for Solving Puzzle Games
Synthesizing Interpretable Strategies for Solving Puzzle Games Eric Butler edbutler@cs.washington.edu Paul G. Allen School of Computer Science and Engineering University of Washington Emina Torlak emina@cs.washington.edu
More informationTransport Capacity and Spectral Efficiency of Large Wireless CDMA Ad Hoc Networks
Transport Capacity and Spectral Efficiency of Large Wireless CDMA Ad Hoc Networks Yi Sun Department of Electrical Engineering The City College of City University of New York Acknowledgement: supported
More informationOptimal Coded Information Network Design and Management via Improved Characterizations of the Binary Entropy Function
Optimal Coded Information Network Design and Management via Improved Characterizations of the Binary Entropy Function John MacLaren Walsh & Steven Weber Department of Electrical and Computer Engineering
More informationPatent Mining: Use of Data/Text Mining for Supporting Patent Retrieval and Analysis
Patent Mining: Use of Data/Text Mining for Supporting Patent Retrieval and Analysis by Chih-Ping Wei ( 魏志平 ), PhD Institute of Service Science and Institute of Technology Management National Tsing Hua
More informationTITLE OF PRESENTATION. Elsevier s Challenge. Dynamic Knowledge Stores and Machine Translation. Presented By Marius Doornenbal,, Anna Tordai
Elsevier s Challenge Dynamic Knowledge Stores and Machine Translation Presented By Marius Doornenbal,, Anna Tordai Date 25-02-2016 OUTLINE Introduction Elsevier: from publisher to a data & analytics company
More informationHow user throughput depends on the traffic demand in large cellular networks
How user throughput depends on the traffic demand in large cellular networks B. Błaszczyszyn Inria/ENS based on a joint work with M. Jovanovic and M. K. Karray (Orange Labs, Paris) 1st Symposium on Spatial
More informationSoilJ Technical Manual
SoilJ Technical Manual Version 0.0.3 2017-09-08 John Koestel Introduction SoilJ is a plugin for the JAVA-based, free and open image processing software ImageJ (Schneider, Rasband, et al., 2012). It is
More informationPredicting Content Virality in Social Cascade
Predicting Content Virality in Social Cascade Ming Cheung, James She, Lei Cao HKUST-NIE Social Media Lab Department of Electronic and Computer Engineering Hong Kong University of Science and Technology,
More informationCS688/WST665 Student presentation Learning Fine-grained Image Similarity with Deep Ranking CVPR Gayoung Lee ( 이가영 )
CS688/WST665 Student presentation Learning Fine-grained Image Similarity with Deep Ranking CVPR 2014 Gayoung Lee ( 이가영 ) Contents 1. Background knowledge 2. Proposed method 3. Experimental Result 4. Conclusion
More informationADAPTIVE ADDER-BASED STEPWISE LINEAR INTERPOLATION
ADAPTIVE ADDER-BASED STEPWISE LINEAR John Moses C Department of Electronics and Communication Engineering, Sreyas Institute of Engineering and Technology, Hyderabad, Telangana, 600068, India. Abstract.
More informationEfficiency and detectability of random reactive jamming in wireless networks
Efficiency and detectability of random reactive jamming in wireless networks Ni An, Steven Weber Modeling & Analysis of Networks Laboratory Drexel University Department of Electrical and Computer Engineering
More informationTime Frequency Domain for Segmentation and Classification of Non-stationary Signals
Time Frequency Domain for Segmentation and Classification of Non-stationary Signals FOCUS SERIES Series Editor Francis Castanié Time Frequency Domain for Segmentation and Classification of Non-stationary
More informationA Practical Approach to Bitrate Control in Wireless Mesh Networks using Wireless Network Utility Maximization
A Practical Approach to Bitrate Control in Wireless Mesh Networks using Wireless Network Utility Maximization EE359 Course Project Mayank Jain Department of Electrical Engineering Stanford University Introduction
More informationArtifacts Reduced Interpolation Method for Single-Sensor Imaging System
2016 International Conference on Computer Engineering and Information Systems (CEIS-16) Artifacts Reduced Interpolation Method for Single-Sensor Imaging System Long-Fei Wang College of Telecommunications
More informationIMAGE PROCESSING IEEE TITLES
2017 2018 IMAGE IEEE TITLES S.no TITLE DOMAIN 1 An Watermarking Scheme Using Threshold Based Secret Sharing 2 Brain Tumor Detection And Segmentation Using Conditional Random Field 3 A Reversible Rie Based
More informationNew Generation Reliability Model
New Generation Reliability Model S.-Y. Liao, C. Huang, T. Guo, A. Chen, Jushan Xie, Cadence Design Systems, Inc. S. Guo, R. Wang, Z. Yu, P. Hao, P. Ren, Y. Wang, R. Huang, Peking University Dec. 5th, 2016
More informationLearning Hierarchical Visual Codebook for Iris Liveness Detection
Learning Hierarchical Visual Codebook for Iris Liveness Detection Hui Zhang 1,2, Zhenan Sun 2, Tieniu Tan 2, Jianyu Wang 1,2 1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences 2.National
More informationAnalysis on Color Filter Array Image Compression Methods
Analysis on Color Filter Array Image Compression Methods Sung Hee Park Electrical Engineering Stanford University Email: shpark7@stanford.edu Albert No Electrical Engineering Stanford University Email:
More informationFace detection, face alignment, and face image parsing
Lecture overview Face detection, face alignment, and face image parsing Brandon M. Smith Guest Lecturer, CS 534 Monday, October 21, 2013 Brief introduction to local features Face detection Face alignment
More informationMicrophone Array Design and Beamforming
Microphone Array Design and Beamforming Heinrich Löllmann Multimedia Communications and Signal Processing heinrich.loellmann@fau.de with contributions from Vladi Tourbabin and Hendrik Barfuss EUSIPCO Tutorial
More informationTwo-stage column generation and applications in container terminal management
Two-stage column generation and applications in container terminal management Ilaria Vacca Matteo Salani Michel Bierlaire Transport and Mobility Laboratory EPFL 8th Swiss Transport Research Conference
More informationCLASSLESS ASSOCIATION USING NEURAL NETWORKS
Workshop track - ICLR 1 CLASSLESS ASSOCIATION USING NEURAL NETWORKS Federico Raue 1,, Sebastian Palacio, Andreas Dengel 1,, Marcus Liwicki 1 1 University of Kaiserslautern, Germany German Research Center
More informationFilters. Materials from Prof. Klaus Mueller
Filters Materials from Prof. Klaus Mueller Think More about Pixels What exactly a pixel is in an image or on the screen? Solid square? This cannot be implemented A dot? Yes, but size matters Pixel Dots
More informationMultiresolution Histograms and their Use for Texture Classification
Multiresolution Histograms and their Use for Texture Classification E. Hadjidemetriou, M. D. Grossberg, and S. K. Nayar Computer Science, Columbia University, New York, NY 17 {stathis, mdog, nayar}@cs.columbia.edu
More informationControl Synthesis and Delay Sensor Deployment for Efficient ASV designs
Control Synthesis and Delay Sensor Deployment for Efficient ASV designs C H A O FA N L I < C H AO F @ TA M U. E D U >, T E X A S A & M U N I V E RS I T Y S A C H I N S. S A PAT N E K A R, U N I V E RS
More informationLiangliang Cao *, Jiebo Luo +, Thomas S. Huang *
Annotating ti Photo Collections by Label Propagation Liangliang Cao *, Jiebo Luo +, Thomas S. Huang * + Kodak Research Laboratories *University of Illinois at Urbana-Champaign (UIUC) ACM Multimedia 2008
More informationChapter - 1 PART - A GENERAL INTRODUCTION
Chapter - 1 PART - A GENERAL INTRODUCTION This chapter highlights the literature survey on the topic of resynthesis of array antennas stating the objective of the thesis and giving a brief idea on how
More informationToday. CS 395T Visual Recognition. Course content. Administration. Expectations. Paper reviews
Today CS 395T Visual Recognition Course logistics Overview Volunteers, prep for next week Thursday, January 18 Administration Class: Tues / Thurs 12:30-2 PM Instructor: Kristen Grauman grauman at cs.utexas.edu
More informationDiversity and Freedom: A Fundamental Tradeoff in Multiple Antenna Channels
Diversity and Freedom: A Fundamental Tradeoff in Multiple Antenna Channels Lizhong Zheng and David Tse Department of EECS, U.C. Berkeley Feb 26, 2002 MSRI Information Theory Workshop Wireless Fading Channels
More informationHistogram-based Threshold Selection of Retinal Feature for Image Registration
Proceeding of IC-ITS 2017 e-isbn:978-967-2122-04-3 Histogram-based Threshold Selection of Retinal Feature for Image Registration Roziana Ramli 1, Mohd Yamani Idna Idris 1 *, Khairunnisa Hasikin 2 & Noor
More informationSparsity-Driven Feature-Enhanced Imaging
Sparsity-Driven Feature-Enhanced Imaging Müjdat Çetin mcetin@mit.edu Faculty of Engineering and Natural Sciences, Sabancõ University, İstanbul, Turkey Laboratory for Information and Decision Systems, Massachusetts
More informationBits From Photons: Oversampled Binary Image Acquisition
Bits From Photons: Oversampled Binary Image Acquisition Feng Yang Audiovisual Communications Laboratory École Polytechnique Fédérale de Lausanne Thesis supervisor: Prof. Martin Vetterli Thesis co-supervisor:
More informationPhysical-Layer Multicasting by Stochastic Beamforming and Alamouti Space-Time Coding
Physical-Layer Multicasting by Stochastic Beamforming and Alamouti Space-Time Coding Anthony Man-Cho So Dept. of Systems Engineering and Engineering Management The Chinese University of Hong Kong (Joint
More informationINTAIRACT: Joint Hand Gesture and Fingertip Classification for Touchless Interaction
INTAIRACT: Joint Hand Gesture and Fingertip Classification for Touchless Interaction Xavier Suau 1,MarcelAlcoverro 2, Adolfo Lopez-Mendez 3, Javier Ruiz-Hidalgo 2,andJosepCasas 3 1 Universitat Politécnica
More informationPrivacy preserving data mining multiplicative perturbation techniques
Privacy preserving data mining multiplicative perturbation techniques Li Xiong CS573 Data Privacy and Anonymity Outline Review and critique of randomization approaches (additive noise) Multiplicative data
More informationA Novel Image Deblurring Method to Improve Iris Recognition Accuracy
A Novel Image Deblurring Method to Improve Iris Recognition Accuracy Jing Liu University of Science and Technology of China National Laboratory of Pattern Recognition, Institute of Automation, Chinese
More information