Presentation: Reviews and talks on ICME 2016
|
|
|
- Ariel Peters
- 5 years ago
- Views:
Transcription
1 Presentation: Reviews and talks on ICME 2016 Cho-Ying Wu Disp Lab Graduate Institute of Communication Engineering National Taiwan University December 07, / 70 Cho-Ying Wu Disp Lab
2 Introduction ICME is a high-class conference on Multimedia. Multimedia? Graphics Speech Vision Display Data Mining Video 2 / 70 Cho-Ying Wu Disp Lab
3 Introduction Top conference on multimedia and related CVPR / ICCV, ECCV, ACM MM Generally, ICME is suitable for MS(30%), PhD(65%) students to challenge. Recruitments from industrial are also a lot. 3 / 70 Cho-Ying Wu Disp Lab
4 Introduction Topics this year : ( Video and Vision are the most popular in the trend) 4 / 70 Cho-Ying Wu Disp Lab
5 Roadmap 1 Impressive Talks (1) Fei-Fei Li, Associate Professor, A Quest for Visual Intelligence. Computer Science Dept. Director, Stanford Artificial Intelligence Lab. (2) Mathias Wien, High Efficiency Video Coding Coding Tools and Specification: HEVC V3 and Coming Developments 2 Interesting Works (1) Gene Cheung, Xian Ming Liu Graph Signal Processing for Image Compression and Restoration, Nii. (2) Xian Ming Liu, Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images, HIT. 5 / 70 Cho-Ying Wu Disp Lab
6 Fei-Fei Li 6 / 70 Cho-Ying Wu Disp Lab
7 Fei-Fei Li 7 / 70 Cho-Ying Wu Disp Lab
8 Fei-Fei Li 8 / 70 Cho-Ying Wu Disp Lab
9 Fei-Fei Li 9 / 70 Cho-Ying Wu Disp Lab
10 Fei-Fei Li 10 / 70 Cho-Ying Wu Disp Lab
11 Fei-Fei Li 11 / 70 Cho-Ying Wu Disp Lab
12 Semantic Segmentation 12 / 70 Cho-Ying Wu Disp Lab
13 Fei-Fei Li 13 / 70 Cho-Ying Wu Disp Lab
14 Fei-Fei Li 14 / 70 Cho-Ying Wu Disp Lab
15 Fei-Fei Li 15 / 70 Cho-Ying Wu Disp Lab
16 Fei-Fei Li 16 / 70 Cho-Ying Wu Disp Lab
17 Fei-Fei Li 17 / 70 Cho-Ying Wu Disp Lab
18 Fei-Fei Li 18 / 70 Cho-Ying Wu Disp Lab
19 Fei-Fei Li 19 / 70 Cho-Ying Wu Disp Lab
20 Fei-Fei Li 20 / 70 Cho-Ying Wu Disp Lab
21 Fei-Fei Li 21 / 70 Cho-Ying Wu Disp Lab
22 Fei-Fei Li 22 / 70 Cho-Ying Wu Disp Lab
23 Fei-Fei Li 23 / 70 Cho-Ying Wu Disp Lab
24 Fei-Fei Li 24 / 70 Cho-Ying Wu Disp Lab
25 Fei-Fei Li 25 / 70 Cho-Ying Wu Disp Lab
26 Fei-Fei Li 26 / 70 Cho-Ying Wu Disp Lab
27 Fei-Fei Li 27 / 70 Cho-Ying Wu Disp Lab
28 Mathias Wien 28 / 70 Cho-Ying Wu Disp Lab
29 Mathias Wien 29 / 70 Cho-Ying Wu Disp Lab
30 Mathias Wien 30 / 70 Cho-Ying Wu Disp Lab
31 Mathias Wien 31 / 70 Cho-Ying Wu Disp Lab
32 Mathias Wien 32 / 70 Cho-Ying Wu Disp Lab
33 Mathias Wien 33 / 70 Cho-Ying Wu Disp Lab
34 Mathias Wien 34 / 70 Cho-Ying Wu Disp Lab
35 Mathias Wien 35 / 70 Cho-Ying Wu Disp Lab
36 Mathias Wien 36 / 70 Cho-Ying Wu Disp Lab
37 Mathias Wien 37 / 70 Cho-Ying Wu Disp Lab
38 Mathias Wien 38 / 70 Cho-Ying Wu Disp Lab
39 Mathias Wien 39 / 70 Cho-Ying Wu Disp Lab
40 Mathias Wien 40 / 70 Cho-Ying Wu Disp Lab
41 Mathias Wien 41 / 70 Cho-Ying Wu Disp Lab
42 Mathias Wien 42 / 70 Cho-Ying Wu Disp Lab
43 Mathias Wien 43 / 70 Cho-Ying Wu Disp Lab
44 Mathias Wien 44 / 70 Cho-Ying Wu Disp Lab
45 Mathias Wien 45 / 70 Cho-Ying Wu Disp Lab
46 Mathias Wien 46 / 70 Cho-Ying Wu Disp Lab
47 Mathias Wien 47 / 70 Cho-Ying Wu Disp Lab
48 Mathias Wien 48 / 70 Cho-Ying Wu Disp Lab
49 Mathias Wien 49 / 70 Cho-Ying Wu Disp Lab
50 Mathias Wien 50 / 70 Cho-Ying Wu Disp Lab
51 Gene Cheung (1) Gene Cheung, Xian Ming Liu Graph Signal Processing for Image Compression and Restoration, Nii. Traditional signal property: 1 1. Discrete 2. Smooth 3. Band-limited frequency Ex. DCT ( Approx. of KLT ) N 1 1 X k xncos n k n 0 N 2 a x Dictionary representation inducing sparse coding 51 / 70 Cho-Ying Wu Disp Lab
52 Gene Cheung Graph Signal : Signal live on graph Ex. Images: 2D grid Research interest: 1. Sampling : how to efficiently acquire signal from graph 2. Representation : Given the graph signal, how to compactly represent it. 3. Signal Restoration : given the partial or noisy signal, how to recover the structure. 52 / 70 Cho-Ying Wu Disp Lab
53 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 53 / 70 Cho-Ying Wu Disp Lab
54 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 54 / 70 Cho-Ying Wu Disp Lab
55 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 55 / 70 Cho-Ying Wu Disp Lab
56 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 56 / 70 Cho-Ying Wu Disp Lab
57 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 57 / 70 Cho-Ying Wu Disp Lab
58 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 58 / 70 Cho-Ying Wu Disp Lab
59 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 59 / 70 Cho-Ying Wu Disp Lab
60 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 60 / 70 Cho-Ying Wu Disp Lab
61 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 61 / 70 Cho-Ying Wu Disp Lab
62 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 D: distortion R: bit-rate Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 62 / 70 Cho-Ying Wu Disp Lab
63 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 63 / 70 Cho-Ying Wu Disp Lab
64 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 64 / 70 Cho-Ying Wu Disp Lab
65 Gene Cheung Graph Laplacian : 1. Adjancency Matrix 1 Remark: Sometimes the original problem is hard to solve, we can change the original problem (primal problem) into dual problem 65 / 70 Cho-Ying Wu Disp Lab
66 Gene Cheung 1 Wei Hu, Gene Cheung, Antonio Ortega, Oscar Au, "Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images," IEEE Transactions on Image Processing, vol.24, no.1, pp , January / 70 Cho-Ying Wu Disp Lab
67 Gene Cheung Skipped part 1 1. PWS image coding : Generalized GFT with intra-coding of H Lifting implementation : lowering complexity from O(N 2 ) to O(Nlog(N)) 3. Image denoising with sparsity and smoothness prior 67 / 70 Cho-Ying Wu Disp Lab
68 Xianming Liu : Soft decoding of JPEG images 1 68 / 70 Cho-Ying Wu Disp Lab
69 Xianming Liu : Soft decoding of JPEG images 1 69 / 70 Cho-Ying Wu Disp Lab
70 Xianming Liu : Soft decoding of JPEG images 1 70 / 70 Cho-Ying Wu Disp Lab
71 Xianming Liu : Soft decoding of JPEG images 1 71 / 70 Cho-Ying Wu Disp Lab
72 Xianming Liu : Soft decoding of JPEG images 1 72 / 70 Cho-Ying Wu Disp Lab
73 Xianming Liu : Soft decoding of JPEG images 1 73 / 70 Cho-Ying Wu Disp Lab
74 Xianming Liu : Soft decoding of JPEG images 1 74 / 70 Cho-Ying Wu Disp Lab
75 Xianming Liu : Soft decoding of JPEG images 1 75 / 70 Cho-Ying Wu Disp Lab
76 Xianming Liu : Soft decoding of JPEG images 1 Ln:Normalized Laplacian See p / 70 Cho-Ying Wu Disp Lab
77 Xianming Liu : Soft decoding of JPEG images 1 77 / 70 Cho-Ying Wu Disp Lab
78 Xianming Liu : Soft decoding of JPEG images 1 78 / 70 Cho-Ying Wu Disp Lab
79 Xianming Liu : Soft decoding of JPEG images 1 79 / 70 Cho-Ying Wu Disp Lab
80 Xianming Liu : Soft decoding of JPEG images 1 80 / 70 Cho-Ying Wu Disp Lab
81 Xianming Liu : Soft decoding of JPEG images 1 81 / 70 Cho-Ying Wu Disp Lab
82 Xianming Liu : Soft decoding of JPEG images 1 82 / 70 Cho-Ying Wu Disp Lab
Graph Signal Processing for Image Compression & Restoration (Part I)
Gene Cheung, Xianming Liu National Institute of Informatics 11 th July, 216 Graph Signal Processing for Image Compression & Restoration (Part I). ICME'16 Tutorial 7/11/216 1 2D video Communication (12
Intra-Prediction and Generalized Graph Fourier Transform for Image Coding
IEEE SIGNAL PROCESSING LETTERS, VOL 22, NO 11, NOVEMBER 2015 1913 Intra-Prediction and Generalized Graph Fourier Transform for Image Coding Wei Hu, Student Member, IEEE, Gene Cheung, Senior Member, IEEE,
BLIND IMAGE DEBLURRING VIA REWEIGHTED GRAPH TOTAL VARIATION
BLIND IMAGE DEBLURRING VIA REWEIGHTED GRAPH TOTAL VARIATION Yuanchao Bai, Gene Cheung, Xianming Liu $, Wen Gao Peking University, Beijing, China, $ Harbin Institute of Technology, Harbin, China National
Interactive Media Streaming Applications Using Merge Frames
Gene Cheung National Institute of Informatics 28 th Novemer, 2017 Interactive Media Streaming Applications Using Merge Frames. BJTU Visit 11/28/2017 1 Acknowledgement Collaorators: M. Kaneko (NII, Japan)
First Attempt of Rapid Compression of 2D Images Based on Histograms Analysis
First Attempt of Rapid Compression of 2D Images Based on Histograms Analysis Danuta Jama Institute of Mathematics Silesian University of Technology Kaszubska 23, 44-100 Gliwice, Poland Email: Danuta.Jama@polsl.pl
JOINT DENOISING AND CONTRAST ENHANCEMENT OF IMAGES USING GRAPH LAPLACIAN OPERATOR
JOINT DENOISING AND CONTRAST ENHANCEMENT OF IMAGES USING GRAPH LAPLACIAN OPERATOR Xiang Liu 1,2, Gene Cheung 2, Xiaolin Wu 3 1 Harbin Institute of Technology, China. 2 National Institute of Informatics,
Automatic Video Caption Detection and Extraction in the DCT Compressed Domain
Automatic Video Caption Detection and Extraction in the DCT Compressed Domain Chin-Fu Tsao 1, Yu-Hao Chen 1, Jin-Hau Kuo 1, Chia-wei Lin 1, and Ja-Ling Wu 1,2 1 Communication and Multimedia Laboratory,
Learning based face hallucination techniques: A survey
Vol. 3 (2014-15) pp. 37-45. : A survey Premitha Premnath K Department of Computer Science & Engineering Vidya Academy of Science & Technology Thrissur - 680501, Kerala, India (email: premithakpnath@gmail.com)
GRAPH-BASED JOINT DENOISING AND SUPER-RESOLUTION OF GENERALIZED PIECEWISE SMOOTH IMAGES
GRAPH-BASED JOINT DENOISING AND SUPER-RESOLUTION OF GENERALIZED PIECEWISE SMOOTH IMAGES Wei Hu, Gene Cheung #, Xin Li $, Oscar C. Au Hong Kong University of Science & Technology, Hong Kong # National Institute
CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM
CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM Yiqun Xu #,Wei Hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang, Siwei Ma,Wen Gao # Institute of Computing Technology, Chinese Academy
Rendering and Modeling of Transparent Objects. Minglun Gong Dept. of CS, Memorial Univ.
Rendering and Modeling of Transparent Objects Minglun Gong Dept. of CS, Memorial Univ. Capture transparent object appearance Using frequency based environmental matting Reduce number of input images needed
Robust Image Watermarking based on DCT-DWT- SVD Method
Robust Image Watermarking based on DCT-DWT- SVD Sneha Jose Rajesh Cherian Roy, PhD. Sreenesh Shashidharan ABSTRACT Hybrid Image watermarking scheme proposed based on Discrete Cosine Transform (DCT)-Discrete
IMAGE DENOISING TO ESTIMATE THE GRADIENT HISTOGRAM PRESERVATION USING VARIOUS ALGORITHMS
IMAGE DENOISING TO ESTIMATE THE GRADIENT HISTOGRAM PRESERVATION USING VARIOUS ALGORITHMS P.Mahalakshmi 1, J.Muthulakshmi 2, S.Kannadhasan 3 1,2 U.G Student, 3 Assistant Professor, Department of Electronics
Learning to Match. Jun Xu, Zhengdong Lu, Tianqi Chen, Hang Li
Learning to Match Jun Xu, Zhengdong Lu, Tianqi Chen, Hang Li 1. Introduction The main tasks in many applications can be formalized as matching between heterogeneous objects, including search, recommendation,
Image Denoising via Group Sparse Eigenvectors of Graph Laplacian
Image Denoising via Group Sparse Eigenvectors of Graph Laplacian Yibin Tang, Ying Chen, Ning Xu, Aimin Jiang, Lin Zhou College of IOT Engineering, Hohai University, Changzhou, China School of Information
Multimodal Information Spaces for Content-based Image Retrieval
Research Proposal Multimodal Information Spaces for Content-based Image Retrieval Abstract Currently, image retrieval by content is a research problem of great interest in academia and the industry, due
Summer Internship at STMicroelectronics, Greater Noida, India
Summer Internship at STMicroelectronics, Greater Noida, India Thibault Lefeuvre, Ecole Polytechnique, France August 2015 Techniques of frame compression in ultra low power encoder Introduction At first,
Keywords - DWT, Lifting Scheme, DWT Processor.
Lifting Based 2D DWT Processor for Image Compression A. F. Mulla, Dr.R. S. Patil aieshamulla@yahoo.com Abstract - Digital images play an important role both in daily life applications as well as in areas
Gabor and Wavelet transforms for signals defined on graphs An invitation to signal processing on graphs
Gabor and Wavelet transforms for signals defined on graphs An invitation to signal processing on graphs Benjamin Ricaud Signal Processing Lab 2, EPFL Lausanne, Switzerland SPLab, Brno, 10/2013 B. Ricaud
Image Analysis & Retrieval. CS/EE 5590 Special Topics (Class Ids: 44873, 44874) Fall 2016, M/W Lec 16
Image Analysis & Retrieval CS/EE 5590 Special Topics (Class Ids: 44873, 44874) Fall 2016, M/W 4-5:15pm@Bloch 0012 Lec 16 Subspace/Transform Optimization Zhu Li Dept of CSEE, UMKC Office: FH560E, Email:
Sparse Coding based Frequency Adaptive Loop Filtering for Video Coding
Sparse Coding based Frequency Adaptive Loop Filtering for Video Coding Outline 1. Sparse Coding based Denoising 2. Frequency Adaptation Model 3. Simulation Setup and Results 4. Summary and Outlook 2 of
Geometry Representations with Unsupervised Feature Learning
Geometry Representations with Unsupervised Feature Learning Yeo-Jin Yoon 1, Alexander Lelidis 2, A. Cengiz Öztireli 3, Jung-Min Hwang 1, Markus Gross 3 and Soo-Mi Choi 1 1 Department of Computer Science
Adaptive Quantization for Video Compression in Frequency Domain
Adaptive Quantization for Video Compression in Frequency Domain *Aree A. Mohammed and **Alan A. Abdulla * Computer Science Department ** Mathematic Department University of Sulaimani P.O.Box: 334 Sulaimani
BIG DATA-DRIVEN FAST REDUCING THE VISUAL BLOCK ARTIFACTS OF DCT COMPRESSED IMAGES FOR URBAN SURVEILLANCE SYSTEMS
BIG DATA-DRIVEN FAST REDUCING THE VISUAL BLOCK ARTIFACTS OF DCT COMPRESSED IMAGES FOR URBAN SURVEILLANCE SYSTEMS Ling Hu and Qiang Ni School of Computing and Communications, Lancaster University, LA1 4WA,
Restoration of Images Corrupted by Mixed Gaussian Impulse Noise with Weighted Encoding
Restoration of Images Corrupted by Mixed Gaussian Impulse Noise with Weighted Encoding Om Prakash V. Bhat 1, Shrividya G. 2, Nagaraj N. S. 3 1 Post Graduation student, Dept. of ECE, NMAMIT-Nitte, Karnataka,
Combinatorial Selection and Least Absolute Shrinkage via The CLASH Operator
Combinatorial Selection and Least Absolute Shrinkage via The CLASH Operator Volkan Cevher Laboratory for Information and Inference Systems LIONS / EPFL http://lions.epfl.ch & Idiap Research Institute joint
Compression of Light Field Images using Projective 2-D Warping method and Block matching
Compression of Light Field Images using Projective 2-D Warping method and Block matching A project Report for EE 398A Anand Kamat Tarcar Electrical Engineering Stanford University, CA (anandkt@stanford.edu)
Graph Fourier Transform for Light Field Compression
Graph Fourier Transform for Light Field Compression Vitor Rosa Meireles Elias and Wallace Alves Martins Abstract This work proposes the use of graph Fourier transform (GFT) for light field data compression.
DIGITAL WATERMARKING FOR GRAY-LEVEL WATERMARKS
DICTA22: Digital Image Computing Techniques and Applications, 2 22 January 22, Melbourne, Australia. DIGITAL WATERMARKING FOR GRAY-LEVEL WATERMARKS *Yuk Ying CHUNG, Man To WONG *Basser Department of Computer
VIDEO WATERMARKING ALGORITHM FOR CONTENT AUTHORISATION
Volume, Issue 3, March 2015 VIDEO WATERMARKING ALGORITHM FOR CONTENT AUTHORISATION V.Rajendhiran J.Elavanthan M.Vengadapathiraj R.Vinothkumar Dr.M.Saravanan Abstract:-In this paper proposed a novel video
Final Review. Image Processing CSE 166 Lecture 18
Final Review Image Processing CSE 166 Lecture 18 Topics covered Basis vectors Matrix based transforms Wavelet transform Image compression Image watermarking Morphological image processing Segmentation
Statistics and Information Technology
Statistics and Information Technology Werner Stuetzle Professor and Chair, Statistics Adjunct Professor, Computer Science and Engineering University of Washington Prepared for NSF Workshop on Statistics:
Implementation of ContourLet Transform For Copyright Protection of Color Images
Implementation of ContourLet Transform For Copyright Protection of Color Images * S.janardhanaRao,** Dr K.Rameshbabu Abstract In this paper, a watermarking algorithm that uses the wavelet transform with
Multibit Digital Watermarking Robust against Local Nonlinear Geometrical Distortions
Multibit Digital Watermarking Robust against Local Nonlinear Geometrical Distortions Sviatoslav Voloshynovskiy Frédéric Deguillaume, and Thierry Pun CUI - University of Geneva 24, rue du Général Dufour
Image denoising in the wavelet domain using Improved Neigh-shrink
Image denoising in the wavelet domain using Improved Neigh-shrink Rahim Kamran 1, Mehdi Nasri, Hossein Nezamabadi-pour 3, Saeid Saryazdi 4 1 Rahimkamran008@gmail.com nasri_me@yahoo.com 3 nezam@uk.ac.ir
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images Honggang Chen Sichuan University honggang chen@yeah.net Shuhua Xiong Sichuan University xiongsh@scu.edu.cn Xiaohai
JPEG Compression Using MATLAB
JPEG Compression Using MATLAB Anurag, Sonia Rani M.Tech Student, HOD CSE CSE Department, ITS Bhiwani India ABSTRACT Creating, editing, and generating s in a very regular system today is a major priority.
AN ANALYTICAL STUDY OF DIFFERENT IMAGE INPAINTING TECHNIQUES
AN ANALYTICAL STUDY OF DIFFERENT IMAGE INPAINTING TECHNIQUES SUPRIYA CHHABRA IT Dept., 245, Guru Premsukh Memorial College of Engineering, Budhpur Village Delhi- 110036 supriyachhabra123@gmail.com RUCHIKA
Optimal Linear Interpolation Coding for Server-based Computing
Optimal Linear Interpolation Coding for Server-based Computing Fei Li and Jason Nieh Network Computing Laboratory Columbia University April 30, 2002 ICC 2002 1 Content 1. Server-based computing 2. Coding
Acommon use of multidimensional transform representations
140 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 56, NO. 1, JANUARY 2008 Incremental Refinement of Computation for the Discrete Wavelet Transform Yiannis Andreopoulos, Member, IEEE, and Mihaela van der
Contour Approximation & Depth Image Coding for Virtual View Synthesis
Contour Approximation & Depth Image Coding for Virtual View Synthesis Yuan Yuan #1, Gene Cheung 2, Pascal rossard &3, Patrick Le Callet %4, Vicky H. Zhao #5 # University of Alberta, Edmonton, A Canada
The most cited papers in Computer Vision
COMPUTER VISION, PUBLICATION The most cited papers in Computer Vision In Computer Vision, Paper Talk on February 10, 2012 at 11:10 pm by gooly (Li Yang Ku) Although it s not always the case that a paper
Robust Image Watermarking Based on Compressed Sensing Techniques
Journal of Information Hiding and Multimedia Signal Processing c 2014 ISSN 2073-4212 Ubiquitous International Volume 5, Number 2, April 2014 Robust Image Watermarking Based on Compressed Sensing Techniques
Research on the New Image De-Noising Methodology Based on Neural Network and HMM-Hidden Markov Models
Research on the New Image De-Noising Methodology Based on Neural Network and HMM-Hidden Markov Models Wenzhun Huang 1, a and Xinxin Xie 1, b 1 School of Information Engineering, Xijing University, Xi an
Stanford University Packing and Padding, Coupled Multi-index for Accurate Image Retrieval.
Liang Zheng Nationality: China Information Systems Technology and Design pillar Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Date of Birth: 11 Jun 1987 Phone: +65 84036274
A Wavelet Tour of Signal Processing The Sparse Way
A Wavelet Tour of Signal Processing The Sparse Way Stephane Mallat with contributions from Gabriel Peyre AMSTERDAM BOSTON HEIDELBERG LONDON NEWYORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY»TOKYO
Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding
2009 11th IEEE International Symposium on Multimedia Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding Ghazaleh R. Esmaili and Pamela C. Cosman Department of Electrical and
A Method of Identifying the P2P File Sharing
IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.11, November 2010 111 A Method of Identifying the P2P File Sharing Jian-Bo Chen Department of Information & Telecommunications
Introduction to Computer Graphics. Image Processing (1) June 8, 2017 Kenshi Takayama
Introduction to Computer Graphics Image Processing (1) June 8, 2017 Kenshi Takayama Today s topics Edge-aware image processing Gradient-domain image processing 2 Image smoothing using Gaussian Filter Smoothness
CV of Qixiang Ye. University of Chinese Academy of Sciences
2012-12-12 University of Chinese Academy of Sciences Qixiang Ye received B.S. and M.S. degrees in mechanical & electronic engineering from Harbin Institute of Technology (HIT) in 1999 and 2001 respectively,
A NEW ROBUST IMAGE WATERMARKING SCHEME BASED ON DWT WITH SVD
A NEW ROBUST IMAGE WATERMARKING SCHEME BASED ON WITH S.Shanmugaprabha PG Scholar, Dept of Computer Science & Engineering VMKV Engineering College, Salem India N.Malmurugan Director Sri Ranganathar Institute
Data Hiding on Text Using Big-5 Code
Data Hiding on Text Using Big-5 Code Jun-Chou Chuang 1 and Yu-Chen Hu 2 1 Department of Computer Science and Communication Engineering Providence University 200 Chung-Chi Rd., Shalu, Taichung 43301, Republic
Multiframe Scene Flow with Piecewise Rigid Motion. Vladislav Golyanik,, Kihwan Kim, Robert Maier, Mathias Nießner, Didier Stricker and Jan Kautz
Multiframe Scene Flow with Piecewise Rigid Motion Vladislav Golyanik,, Kihwan Kim, Robert Maier, Mathias Nießner, Didier Stricker and Jan Kautz Scene Flow. 2 Scene Flow. 3 Scene Flow. Scene Flow Estimation:
Multi Focus Image Fusion Using Joint Sparse Representation
Multi Focus Image Fusion Using Joint Sparse Representation Prabhavathi.P 1 Department of Information Technology, PG Student, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India 1 ABSTRACT: The
Overcompressing JPEG images with Evolution Algorithms
Author manuscript, published in "EvoIASP2007, Valencia : Spain (2007)" Overcompressing JPEG images with Evolution Algorithms Jacques Lévy Véhel 1, Franklin Mendivil 2 and Evelyne Lutton 1 1 Inria, Complex
An Introduction to Pattern Recognition
An Introduction to Pattern Recognition Speaker : Wei lun Chao Advisor : Prof. Jian-jiun Ding DISP Lab Graduate Institute of Communication Engineering 1 Abstract Not a new research field Wide range included
Image Compression Algorithm and JPEG Standard
International Journal of Scientific and Research Publications, Volume 7, Issue 12, December 2017 150 Image Compression Algorithm and JPEG Standard Suman Kunwar sumn2u@gmail.com Summary. The interest in
Compressive Sensing based image processing in TrapView pest monitoring system
Compressive Sensing based image processing in TrapView pest monitoring system Milan Marić *, Irena Orović ** and Srdjan Stanković ** * S&T Crna Gora d.o.o, Podgorica, Montenegro ** University of Montenegro,
Ph.D. in Computer Science (
Computer Science 1 COMPUTER SCIENCE http://www.cs.miami.edu Dept. Code: CSC Introduction The Department of Computer Science offers undergraduate and graduate education in Computer Science, and performs
A Reversible Data Hiding Scheme for BTC- Compressed Images
IJACSA International Journal of Advanced Computer Science and Applications, A Reversible Data Hiding Scheme for BTC- Compressed Images Ching-Chiuan Lin Shih-Chieh Chen Department of Multimedia and Game
PAPER Optimal Quantization Parameter Set for MPEG-4 Bit-Rate Control
3338 PAPER Optimal Quantization Parameter Set for MPEG-4 Bit-Rate Control Dong-Wan SEO, Seong-Wook HAN, Yong-Goo KIM, and Yoonsik CHOE, Nonmembers SUMMARY In this paper, we propose an optimal bit rate
Graph-based Transforms for Predictive Light Field Compression based on Super-Pixels
Graph-based Transforms for Predictive Light Field Compression based on Super-Pixels Mira Rizkallah, Xin Su, Thomas Maugey, Christine Guillemot To cite this version: Mira Rizkallah, Xin Su, Thomas Maugey,
A Novel Statistical Distortion Model Based on Mixed Laplacian and Uniform Distribution of Mpeg-4 FGS
A Novel Statistical Distortion Model Based on Mixed Laplacian and Uniform Distribution of Mpeg-4 FGS Xie Li and Wenjun Zhang Institute of Image Communication and Information Processing, Shanghai Jiaotong
FREE-FORM SURFACE DESIGN: SUBDIVISION + SMOOTHING (4) 20 STEPS OF NEW ALGORITHM WITH = 0:33 = 0:34 FREE-FORM SURFACE DESIGN: SUBDIVISION + SMOOTHING (
SMOOTH INTERPOLATION (2) ONE CONSTRAINT (x N C ) 1 = x 1 WRITE DESIRED CONSTRAINED SMOOTH SIGNAL x N AS SUM OF C UNCONSTRAINED SMOOTH SIGNAL x N = Fx (F = f(k) N ) PLUS SMOOTH DEFORMATION d 1 x N C = xn
SUPPLEMENTARY MATERIAL
SUPPLEMENTARY MATERIAL Zhiyuan Zha 1,3, Xin Liu 2, Ziheng Zhou 2, Xiaohua Huang 2, Jingang Shi 2, Zhenhong Shang 3, Lan Tang 1, Yechao Bai 1, Qiong Wang 1, Xinggan Zhang 1 1 School of Electronic Science
SSD: Single Shot MultiBox Detector. Author: Wei Liu et al. Presenter: Siyu Jiang
SSD: Single Shot MultiBox Detector Author: Wei Liu et al. Presenter: Siyu Jiang Outline 1. Motivations 2. Contributions 3. Methodology 4. Experiments 5. Conclusions 6. Extensions Motivation Motivation
Comparative Analysis of Image Compression Using Wavelet and Ridgelet Transform
Comparative Analysis of Image Compression Using Wavelet and Ridgelet Transform Thaarini.P 1, Thiyagarajan.J 2 PG Student, Department of EEE, K.S.R College of Engineering, Thiruchengode, Tamil Nadu, India
A STAIRCASE TRANSFORM CODING SCHEME FOR SCREEN CONTENT VIDEO CODING. Cheng Chen, Jingning Han, Yaowu Xu, and James Bankoski
A STAIRCASE TRANSFORM CODING SCHEME FOR SCREEN CONTENT VIDEO CODING Cheng Chen, Jingning Han, Yaowu Xu, and James Bankoski WebM Codec Team, Google Inc. 6 Amphitheatre Parkway, Mountain View, CA 9443 Emails:
Visual Material Traits Recognizing Per-Pixel Material Context
Visual Material Traits Recognizing Per-Pixel Material Context Gabriel Schwartz gbs25@drexel.edu Ko Nishino kon@drexel.edu 1 / 22 2 / 22 Materials as Visual Context What tells us this road is unsafe? 3
Sparse Component Analysis (SCA) in Random-valued and Salt and Pepper Noise Removal
Sparse Component Analysis (SCA) in Random-valued and Salt and Pepper Noise Removal Hadi. Zayyani, Seyyedmajid. Valliollahzadeh Sharif University of Technology zayyani000@yahoo.com, valliollahzadeh@yahoo.com
FPGA IMPLEMENTATION OF HIGH SPEED DCT COMPUTATION OF JPEG USING VEDIC MULTIPLIER
FPGA IMPLEMENTATION OF HIGH SPEED DCT COMPUTATION OF JPEG USING VEDIC MULTIPLIER Prasannkumar Sohani Department of Electronics Shivaji University, Kolhapur, Maharashtra, India P.C.Bhaskar Department of
Concealing Information in Images using Progressive Recovery
Concealing Information in Images using Progressive Recovery Pooja R 1, Neha S Prasad 2, Nithya S Jois 3, Sahithya KS 4, Bhagyashri R H 5 1,2,3,4 UG Student, Department Of Computer Science and Engineering,
Measurements and Bits: Compressed Sensing meets Information Theory. Dror Baron ECE Department Rice University dsp.rice.edu/cs
Measurements and Bits: Compressed Sensing meets Information Theory Dror Baron ECE Department Rice University dsp.rice.edu/cs Sensing by Sampling Sample data at Nyquist rate Compress data using model (e.g.,
ANALYSIS OF SPIHT ALGORITHM FOR SATELLITE IMAGE COMPRESSION
ANALYSIS OF SPIHT ALGORITHM FOR SATELLITE IMAGE COMPRESSION K Nagamani (1) and AG Ananth (2) (1) Assistant Professor, R V College of Engineering, Bangalore-560059. knmsm_03@yahoo.com (2) Professor, R V
Survey of the Mathematics of Big Data
Survey of the Mathematics of Big Data Issues with Big Data, Mathematics to the Rescue Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) Math & Big Data Fall 2015 1 / 28 Introduction We survey some
Robust Image Watermarking based on Discrete Wavelet Transform, Discrete Cosine Transform & Singular Value Decomposition
Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 8 (2013), pp. 971-976 Research India Publications http://www.ripublication.com/aeee.htm Robust Image Watermarking based
An Image Coding Approach Using Wavelet-Based Adaptive Contourlet Transform
009 International Joint Conference on Computational Sciences and Optimization An Image Coding Approach Using Wavelet-Based Adaptive Contourlet Transform Guoan Yang, Zhiqiang Tian, Chongyuan Bi, Yuzhen
Parameter Free Image Artifacts Detection: A Compression Based Approach
Parameter Free Image Artifacts Detection: A Compression Based Approach Avid Roman-Gonzalez a*, Mihai Datcu b a TELECOM ParisTech, 75013 Paris, France b German Aerospace Center (DLR), Oberpfaffenhofen ABSTRACT
Video Compression Method for On-Board Systems of Construction Robots
Video Compression Method for On-Board Systems of Construction Robots Andrei Petukhov, Michael Rachkov Moscow State Industrial University Department of Automatics, Informatics and Control Systems ul. Avtozavodskaya,
Efficient and Low-Complexity Image Coding with the Lifting Scheme and Modified SPIHT
Efficient and Low-Complexity Image Coding with the Lifting Scheme and Modified SPIHT Hong Pan, W.C. Siu, and N.F. Law Abstract In this paper, we propose an efficient and low complexity image coding algorithm
Image Transformation Techniques Dr. Rajeev Srivastava Dept. of Computer Engineering, ITBHU, Varanasi
Image Transformation Techniques Dr. Rajeev Srivastava Dept. of Computer Engineering, ITBHU, Varanasi 1. Introduction The choice of a particular transform in a given application depends on the amount of
Optimizing the Deblocking Algorithm for. H.264 Decoder Implementation
Optimizing the Deblocking Algorithm for H.264 Decoder Implementation Ken Kin-Hung Lam Abstract In the emerging H.264 video coding standard, a deblocking/loop filter is required for improving the visual
ISSN: (Online) Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
Lecture 18: Human Motion Recognition
Lecture 18: Human Motion Recognition Professor Fei Fei Li Stanford Vision Lab 1 What we will learn today? Introduction Motion classification using template matching Motion classification i using spatio
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction Marc Pollefeys Joined work with Nikolay Savinov, Christian Haene, Lubor Ladicky 2 Comparison to Volumetric Fusion Higher-order ray
VLSI DESIGN APPROACH FOR IMAGE COMPRESSION USING WAVELET
VLSI DESIGN APPROACH FOR IMAGE COMPRESSION USING WAVELET 1 S. S. Mungona, 2 Dr. S. A. Ladhake 1 Assistant Professor, Department of Electronics And Telecommunication, Sipna College of Engineering and Technology,
Sparse Reconstruction / Compressive Sensing
Sparse Reconstruction / Compressive Sensing Namrata Vaswani Department of Electrical and Computer Engineering Iowa State University Namrata Vaswani Sparse Reconstruction / Compressive Sensing 1/ 20 The
Digital Signal Processing
Digital Signal Processing 20 (2010) 1629 1636 Contents lists available at ScienceDirect Digital Signal Processing wwwelseviercom/locate/dsp High capacity lossless data embedding technique for palette images
Module 9 AUDIO CODING. Version 2 ECE IIT, Kharagpur
Module 9 AUDIO CODING Lesson 29 Transform and Filter banks Instructional Objectives At the end of this lesson, the students should be able to: 1. Define the three layers of MPEG-1 audio coding. 2. Define
Image Analysis & Retrieval. CS/EE 5590 Special Topics (Class Ids: 44873, 44874) Fall 2016, M/W Lec 18.
Image Analysis & Retrieval CS/EE 5590 Special Topics (Class Ids: 44873, 44874) Fall 2016, M/W 4-5:15pm@Bloch 0012 Lec 18 Image Hashing Zhu Li Dept of CSEE, UMKC Office: FH560E, Email: lizhu@umkc.edu, Ph:
Digital Communication Prof. Bikash Kumar Dey Department of Electrical Engineering Indian Institute of Technology, Bombay
Digital Communication Prof. Bikash Kumar Dey Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture - 26 Source Coding (Part 1) Hello everyone, we will start a new module today
Study on Jabber Be Applied to Video Diagnosis for Plant Diseases and Insect Pests
Study on Jabber Be Applied to Video Diagnosis for Plant Diseases and Insect Pests Wei Zhang *, JunFeng Zhang, Feng Yu, JiChun Zhao, and RuPeng Luan Agriculture and Forestry Academy of Beijing; Beijing
Department of Electronics and Communication KMP College of Engineering, Perumbavoor, Kerala, India 1 2
Vol.3, Issue 3, 2015, Page.1115-1021 Effect of Anti-Forensics and Dic.TV Method for Reducing Artifact in JPEG Decompression 1 Deepthy Mohan, 2 Sreejith.H 1 PG Scholar, 2 Assistant Professor Department
JOINT DENOISING / COMPRESSION OF IMAGE CONTOURS VIA GEOMETRIC PRIOR AND VARIABLE-LENGTH CONTEXT TREE. Amin Zheng, Gene Cheung #, Dinei Florencio $
JOINT DENOISING / COMPRESSION OF IMAGE CONTOURS VIA GEOMETRIC PRIOR AND VARIABLE-LENGTH CONTEXT TREE Amin Zheng, Gene Cheung #, Dinei Florencio $ Hong Kong University of Science and Technology, # National
Denoising and Edge Detection Using Sobelmethod
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Denoising and Edge Detection Using Sobelmethod P. Sravya 1, T. Rupa devi 2, M. Janardhana Rao 3, K. Sai Jagadeesh 4, K. Prasanna
Data Hiding in Video
Data Hiding in Video J. J. Chae and B. S. Manjunath Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 9316-956 Email: chaejj, manj@iplab.ece.ucsb.edu Abstract
A Miniature-Based Image Retrieval System
A Miniature-Based Image Retrieval System Md. Saiful Islam 1 and Md. Haider Ali 2 Institute of Information Technology 1, Dept. of Computer Science and Engineering 2, University of Dhaka 1, 2, Dhaka-1000,
Shape Preserving RGB-D Depth Map Restoration
Shape Preserving RGB-D Depth Map Restoration Wei Liu 1, Haoyang Xue 1, Yun Gu 1, Qiang Wu 2, Jie Yang 1, and Nikola Kasabov 3 1 The Key Laboratory of Ministry of Education for System Control and Information
Multimedia Systems Image III (Image Compression, JPEG) Mahdi Amiri April 2011 Sharif University of Technology
Course Presentation Multimedia Systems Image III (Image Compression, JPEG) Mahdi Amiri April 2011 Sharif University of Technology Image Compression Basics Large amount of data in digital images File size
Structure-adaptive Image Denoising with 3D Collaborative Filtering
, pp.42-47 http://dx.doi.org/10.14257/astl.2015.80.09 Structure-adaptive Image Denoising with 3D Collaborative Filtering Xuemei Wang 1, Dengyin Zhang 2, Min Zhu 2,3, Yingtian Ji 2, Jin Wang 4 1 College