# Video Texture. A.A. Efros

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Video Texture A.A. Efros : Computational Photography Alexei Efros, CMU, Fall 2005

2 Weather Forecasting for Dummies Let s predict weather: Given today s weather only, we want to know tomorrow s Suppose weather can only be {Sunny, Cloudy, Raining} The Weather Channel algorithm: Over a long period of time, record: How often S followed by R How often S followed by S Etc. Compute percentages for each state: P(R S), P(S S), etc. Predict the state with highest probability! It s a Markov Chain

3 Markov Chain What if we know today and yestarday s weather?

4 Text Synthesis [Shannon, 48] proposed a way to generate English-looking text using N-grams: Assume a generalized Markov model Use a large text to compute prob. distributions of each letter given N-1 previous letters Starting from a seed repeatedly sample this Markov chain to generate new letters Also works for whole words WE NEED TO EAT CAKE

5 Mark V. Shaney (Bell Labs) Results (using alt.singles corpus): As I've commented before, really relating to someone involves standing next to impossible. One morning I shot an elephant in my arms and kissed him. I spent an interesting evening recently with a grain of salt

6 Video Textures Arno Schödl Richard Szeliski David Salesin Irfan Essa Microsoft Research, Georgia Tech

7 Still photos

8 Video clips

9 Video textures

10 Problem statement video clip video texture

11 Our approach How do we find good transitions?

12 Finding good transitions Compute L 2 distance D i, j between all frames vs. frame i frame j Similar frames make good transitions

13 Markov chain representation Similar frames make good transitions

14 Transition costs Transition from i to j if successor of i is similar to j Cost function: C i j = D i+1, j i i+1 i j D i+1, j j-1 j

15 Transition probabilities Probability for transition P i j inversely related to cost: P i j ~exp ( C i j / σ 2 ) high σ low σ

16 Preserving dynamics

17 Preserving dynamics

18 Preserving dynamics Cost for transition i j N-1 C i j = Σ w k D i+k+1, j+k k = -N i-1 i i+1 i+2 D i-1, j-2 D i, j-1 i j D D i+1, j i+2, j+1 j-2 j-1 j j+1

19 Preserving dynamics effect Cost for transition i j N-1 C i j = Σ w k D i+k+1, j+k k = -N

20 Dead ends No good transition at the end of sequence

21 Future cost Propagate future transition costs backward Iteratively compute new cost F i j = C i j + α min k F j k

22 Future cost Propagate future transition costs backward Iteratively compute new cost F i j = C i j + α min k F j k

23 Future cost Propagate future transition costs backward Iteratively compute new cost F i j = C i j + α min k F j k

24 Future cost Propagate future transition costs backward Iteratively compute new cost F i j = C i j + α min k F j k

25 Future cost Propagate future transition costs backward Iteratively compute new cost F i j = C i j + α min k F j k Q-learning

26 Future cost effect

27 Finding good loops Alternative to random transitions Precompute set of loops up front

28 Visual discontinuities Problem: Visible Jumps

29 Crossfading Solution: Crossfade from one sequence to the other. A i A i A 4 i 4 A i B 4 j-2 + B + 4 j-1 4 B j B j+1 A i-2 15 A i-1 /B j-2 A i-1 /B j-2 A i-1 /B j-2 B j+1

30 Morphing Interpolation task: 2 A B C

31 Morphing Interpolation task: 2 A B C Compute correspondence between pixels of all frames

32 Morphing Interpolation task: 2 A B C Compute correspondence between pixels of all frames Interpolate pixel position and color in morphed frame based on [Shum 2000]

37 Video portrait Useful for web pages

38 Region-based analysis Divide video up into regions Generate a video texture for each region

39 Automatic region analysis

40 Video-based animation Like sprites computer games Extract sprites from real video Interactively control desired motion 1985 Nintendo of America Inc.

41 Video sprite extraction blue screen matting and velocity estimation

42 Video sprite control Augmented transition cost: Animation C i j = α + β angle C i j Similarity term Control term vector to mouse pointer velocity vector

43 Interactive fish

44 Lord of the Flies

45 Summary Video clips video textures define Markov process preserve dynamics avoid dead-ends disguise visual discontinuities

46 Motion Analysis & Synthesis [Efros 03] What are they doing? Activity recognition, surveillance, anti-terrorism Can we do the same? Motion retargeting, movies, video games, etc.

47 Gathering action data Low resolution, noisy data Moving camera Occlusions

48 Figure-centric Representation Stabilized spatio-temporal volume No translation information All motion caused by person s limbs Good news: indifferent to camera motion Bad news: hard! Good test to see if actions, not just translation, are being captured

49 Remembrance of Things Past Explain novel motion sequence with bits and pieces of previously seen video clips input sequence run walk left motion analysis swing walk right motion synthesis jog synthetic sequence Challenge: how to compare motions?

50 How to describe motion? Appearance Not preserved across different clothing Gradients (spatial, temporal) same (e.g. contrast reversal) Edges Too unreliable Optical flow Explicitly encodes motion Least affected by appearance but too noisy

51 Motion Descriptor Image frame Optical flow F x, y F x, F y F, F, F, F blurred x + x y + y F, F, F, F x + x y + y

52 Comparing motion descriptors Σ t I matrix frame-to-frame similarity matrix blurry I motion-to-motion similarity matrix

53 Recognizing Tennis Red bars show classification results

54 Do as I Do Motion Synthesis input sequence synthetic sequence Matching two things: Motion similarity across sequences Appearance similarity within sequence Dynamic Programming

55 Smoothness for Synthesis is similarity between source and target frames W act is appearance similarity within target frames W app For every source frame i, find best target frame by maximizing following cost function: π i n i= 1 α act W act ( i, π ) i + n i= 2 α app W app ( π, π i i 1 + 1) Optimize using dynamic programming

56 Do as I Do Source Motion Source Appearance 3400 Frames Result

57 Do as I Say Synthesis run walk left swing walk right jog run walk left swing walk right jog synthetic sequence Synthesize given action labels e.g. video game control

58 Do as I Say Red box shows when constraint is applied

59 Application: Motion Retargeting Rendering new character into existing footage Algorithm Track original character Find matches from new character Erase original character Render in new character Need to worry about occlusions

60 Context-based Image Correction Input sequence 3 closest frames median images

61 Actor Replacement SHOW VIDEO

### Topics. Image Processing Techniques and Smart Image Manipulation. Texture Synthesis. Topics. Markov Chain. Weather Forecasting for Dummies

Image Processing Techniques and Smart Image Manipulation Maneesh Agrawala Topics Texture Synthesis High Dynamic Range Imaging Bilateral Filter Gradient-Domain Techniques Matting Graph-Cut Optimization

### Image Processing Techniques and Smart Image Manipulation : Texture Synthesis

CS294-13: Special Topics Lecture #15 Advanced Computer Graphics University of California, Berkeley Monday, 26 October 2009 Image Processing Techniques and Smart Image Manipulation : Texture Synthesis Lecture

### Machine Learning for Video-Based Rendering

Machine Learning for Video-Based Rendering Arno Schadl arno@schoedl. org Irfan Essa irjan@cc.gatech.edu Georgia Institute of Technology GVU Center / College of Computing Atlanta, GA 30332-0280, USA. Abstract

### Lecture 6: Texture. Tuesday, Sept 18

Lecture 6: Texture Tuesday, Sept 18 Graduate students Problem set 1 extension ideas Chamfer matching Hierarchy of shape prototypes, search over translations Comparisons with Hausdorff distance, L1 on

### Epitomic Analysis of Human Motion

Epitomic Analysis of Human Motion Wooyoung Kim James M. Rehg Department of Computer Science Georgia Institute of Technology Atlanta, GA 30332 {wooyoung, rehg}@cc.gatech.edu Abstract Epitomic analysis is

### Inferring 3D People from 2D Images

Inferring 3D People from 2D Images Department of Computer Science Brown University http://www.cs.brown.edu/~black Collaborators Hedvig Sidenbladh, Swedish Defense Research Inst. Leon Sigal, Brown University

### Synthesizing Realistic Facial Expressions from Photographs

Synthesizing Realistic Facial Expressions from Photographs 1998 F. Pighin, J Hecker, D. Lischinskiy, R. Szeliskiz and D. H. Salesin University of Washington, The Hebrew University Microsoft Research 1

### Image Warping and Morphing. Alexey Tikhonov : Computational Photography Alexei Efros, CMU, Fall 2007

Image Warping and Morphing Alexey Tikhonov 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 Image Warping in Biology D'Arcy Thompson http://www-groups.dcs.st-and.ac.uk/~history/miscellaneous/darcy.html

### COMPUTER VISION FOR VISUAL EFFECTS

COMPUTER VISION FOR VISUAL EFFECTS Modern blockbuster movies seamlessly introduce impossible characters and action into real-world settings using digital visual effects. These effects are made possible

### 3D Editing System for Captured Real Scenes

3D Editing System for Captured Real Scenes Inwoo Ha, Yong Beom Lee and James D.K. Kim Samsung Advanced Institute of Technology, Youngin, South Korea E-mail: {iw.ha, leey, jamesdk.kim}@samsung.com Tel:

### Course Outline. Advanced Computer Graphics. Animation. The Story So Far. Animation. To Do

Advanced Computer Graphics CSE 163 [Spring 2017], Lecture 18 Ravi Ramamoorthi http://www.cs.ucsd.edu/~ravir 3D Graphics Pipeline Modeling (Creating 3D Geometry) Course Outline Rendering (Creating, shading

### Channels & Keyframes. CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2017

Channels & Keyframes CSE69: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 27 Animation Rigging and Animation Animation System Pose Φ... 2 N Rigging System Triangles Renderer Animation When

### 3D Human Motion Analysis and Manifolds

D E P A R T M E N T O F C O M P U T E R S C I E N C E U N I V E R S I T Y O F C O P E N H A G E N 3D Human Motion Analysis and Manifolds Kim Steenstrup Pedersen DIKU Image group and E-Science center Motivation

### Animation. Itinerary Computer Graphics Lecture 22

15-462 Computer Graphics Lecture 22 Animation April 22, 2003 M. Ian Graham Carnegie Mellon University Itinerary Review Basic Animation Keyed Animation Motion Capture Physically-Based Animation Behavioral

### Motion Synthesis and Editing. Yisheng Chen

Motion Synthesis and Editing Yisheng Chen Overview Data driven motion synthesis automatically generate motion from a motion capture database, offline or interactive User inputs Large, high-dimensional

### Texture Sensitive Image Inpainting after Object Morphing

Texture Sensitive Image Inpainting after Object Morphing Yin Chieh Liu and Yi-Leh Wu Department of Computer Science and Information Engineering National Taiwan University of Science and Technology, Taiwan

### Beyond Mere Pixels: How Can Computers Interpret and Compare Digital Images? Nicholas R. Howe Cornell University

Beyond Mere Pixels: How Can Computers Interpret and Compare Digital Images? Nicholas R. Howe Cornell University Why Image Retrieval? World Wide Web: Millions of hosts Billions of images Growth of video

### Panoramic Video Texture

Aseem Agarwala, Colin Zheng, Chris Pal, Maneesh Agrawala, Michael Cohen, Brian Curless, David Salesin, Richard Szeliski A paper accepted for SIGGRAPH 05 presented by 1 Outline Introduction & Motivation

### arxiv: v1 [cs.cv] 22 Feb 2017

Synthesising Dynamic Textures using Convolutional Neural Networks arxiv:1702.07006v1 [cs.cv] 22 Feb 2017 Christina M. Funke, 1, 2, 3, Leon A. Gatys, 1, 2, 4, Alexander S. Ecker 1, 2, 5 1, 2, 3, 6 and Matthias

### Image-Based Modeling and Rendering

Image-Based Modeling and Rendering Richard Szeliski Microsoft Research IPAM Graduate Summer School: Computer Vision July 26, 2013 How far have we come? Light Fields / Lumigraph - 1996 Richard Szeliski

### Normalized cuts and image segmentation

Normalized cuts and image segmentation Department of EE University of Washington Yeping Su Xiaodan Song Normalized Cuts and Image Segmentation, IEEE Trans. PAMI, August 2000 5/20/2003 1 Outline 1. Image

### Overview. Video. Overview 4/7/2008. Optical flow. Why estimate motion? Motion estimation: Optical flow. Motion Magnification Colorization.

Overview Video Optical flow Motion Magnification Colorization Lecture 9 Optical flow Motion Magnification Colorization Overview Optical flow Combination of slides from Rick Szeliski, Steve Seitz, Alyosha

Animation by Adaptation Michael Gleicher Graphics Group Department of Computer Sciences University of Wisconsin Madison http://www.cs.wisc.edu/graphics The Dream: Animation for Everyone! Animation is great

### Animated People Textures

Animated People Textures Bhrigu Celly and Victor B. Zordan Riverside Graphic Lab University of California, Riverside {bcelly,vbz}@cs.ucr.edu www.cs.ucr.edu/rgl Abstract This paper introduces a technique

### Reconstruction of Images Distorted by Water Waves

Reconstruction of Images Distorted by Water Waves Arturo Donate and Eraldo Ribeiro Computer Vision Group Outline of the talk Introduction Analysis Background Method Experiments Conclusions Future Work

### Data-driven Approaches to Simulation (Motion Capture)

1 Data-driven Approaches to Simulation (Motion Capture) Ting-Chun Sun tingchun.sun@usc.edu Preface The lecture slides [1] are made by Jessica Hodgins [2], who is a professor in Computer Science Department

### Faces. Face Modeling. Topics in Image-Based Modeling and Rendering CSE291 J00 Lecture 17

Face Modeling Topics in Image-Based Modeling and Rendering CSE291 J00 Lecture 17 Faces CS291-J00, Winter 2003 From David Romdhani Kriegman, slides 2003 1 Approaches 2-D Models morphing, indexing, etc.

### Face Modeling. Portrait of Piotr Gibas Joaquin Rosales Gomez

Face Modeling Portrait of Piotr Gibas Joaquin Rosales Gomez 15-463: Computational Photography Alexei Efros, CMU, Fall 2006 The Power of Averaging Figure-centric averages Antonio Torralba & Aude Oliva (2002)

### Global Flow Estimation. Lecture 9

Global Flow Estimation Lecture 9 Global Motion Estimate motion using all pixels in the image. Parametric flow gives an equation, which describes optical flow for each pixel. Affine Projective Global motion

### Thiruvarangan Ramaraj CS525 Graphics & Scientific Visualization Spring 2007, Presentation I, February 28 th 2007, 14:10 15:00. Topic (Research Paper):

Thiruvarangan Ramaraj CS525 Graphics & Scientific Visualization Spring 2007, Presentation I, February 28 th 2007, 14:10 15:00 Topic (Research Paper): Jinxian Chai and Jessica K. Hodgins, Performance Animation

### Region-based Segmentation

Region-based Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc.

### Learning Silhouette Features for Control of Human Motion

Learning Silhouette Features for Control of Human Motion Liu Ren 1, Gregory Shakhnarovich 2, Jessica K. Hodgins 1 Hanspeter Pfister 3, Paul A. Viola 4 July 2004 CMU-CS-04-165 School of Computer Science

### Digital Image Processing COSC 6380/4393

Digital Image Processing COSC 6380/4393 Lecture 21 Nov 16 th, 2017 Pranav Mantini Ack: Shah. M Image Processing Geometric Transformation Point Operations Filtering (spatial, Frequency) Input Restoration/

### Image-Based Deformation of Objects in Real Scenes

Image-Based Deformation of Objects in Real Scenes Han-Vit Chung and In-Kwon Lee Dept. of Computer Science, Yonsei University sharpguy@cs.yonsei.ac.kr, iklee@yonsei.ac.kr Abstract. We present a new method

### Interpolation, extrapolation, etc.

Interpolation, extrapolation, etc. Prof. Ramin Zabih http://cs100r.cs.cornell.edu Administrivia Assignment 5 is out, due Wednesday A6 is likely to involve dogs again Monday sections are now optional Will

### animation projects in digital art animation 2009 fabio pellacini 1

animation projects in digital art animation 2009 fabio pellacini 1 animation shape specification as a function of time projects in digital art animation 2009 fabio pellacini 2 how animation works? flip

### Introduction to Image Super-resolution. Presenter: Kevin Su

Introduction to Image Super-resolution Presenter: Kevin Su References 1. S.C. Park, M.K. Park, and M.G. KANG, Super-Resolution Image Reconstruction: A Technical Overview, IEEE Signal Processing Magazine,

### CS 4495 Computer Vision A. Bobick. Motion and Optic Flow. Stereo Matching

Stereo Matching Fundamental matrix Let p be a point in left image, p in right image l l Epipolar relation p maps to epipolar line l p maps to epipolar line l p p Epipolar mapping described by a 3x3 matrix

### Video based Animation Synthesis with the Essential Graph. Adnane Boukhayma, Edmond Boyer MORPHEO INRIA Grenoble Rhône-Alpes

Video based Animation Synthesis with the Essential Graph Adnane Boukhayma, Edmond Boyer MORPHEO INRIA Grenoble Rhône-Alpes Goal Given a set of 4D models, how to generate realistic motion from user specified

### Aircraft Tracking Based on KLT Feature Tracker and Image Modeling

Aircraft Tracking Based on KLT Feature Tracker and Image Modeling Khawar Ali, Shoab A. Khan, and Usman Akram Computer Engineering Department, College of Electrical & Mechanical Engineering, National University

### 3D Face and Hand Tracking for American Sign Language Recognition

3D Face and Hand Tracking for American Sign Language Recognition NSF-ITR (2004-2008) D. Metaxas, A. Elgammal, V. Pavlovic (Rutgers Univ.) C. Neidle (Boston Univ.) C. Vogler (Gallaudet) The need for automated

### Character Animation COS 426

Character Animation COS 426 Syllabus I. Image processing II. Modeling III. Rendering IV. Animation Image Processing (Rusty Coleman, CS426, Fall99) Rendering (Michael Bostock, CS426, Fall99) Modeling (Dennis

### Motion Magnification

Motion Magnification Ce Liu Antonio Torralba William T. Freeman Fre do Durand Edward H. Adelson Computer Science and Artificial Intelligence Lab (CSAIL) Massachusetts Institute of Technology (a) Input

### Deep Learning for Visual Manipulation and Synthesis

Deep Learning for Visual Manipulation and Synthesis Jun-Yan Zhu 朱俊彦 UC Berkeley 2017/01/11 @ VALSE What is visual manipulation? Image Editing Program input photo User Input result Desired output: stay

### Motion Analysis. Motion analysis. Now we will talk about. Differential Motion Analysis. Motion analysis. Difference Pictures

Now we will talk about Motion Analysis Motion analysis Motion analysis is dealing with three main groups of motionrelated problems: Motion detection Moving object detection and location. Derivation of

### Towards Real-Time Texture Synthesis with the Jump Map

Thirteenth Eurographics Workshop on Rendering (2002) P. Debevec and S. Gibson (Editors) Towards Real-Time Texture Synthesis with the Jump Map Steve Zelinka and Michael Garland Department of Computer Science,

### BCC Textured Wipe Animation menu Manual Auto Pct. Done Percent Done

BCC Textured Wipe The BCC Textured Wipe creates is a non-geometric wipe using the Influence layer and the Texture settings. By default, the Influence is generated from the luminance of the outgoing clip

### Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation

Obviously, this is a very slow process and not suitable for dynamic scenes. To speed things up, we can use a laser that projects a vertical line of light onto the scene. This laser rotates around its vertical

### Fundamentals of Stereo Vision Michael Bleyer LVA Stereo Vision

Fundamentals of Stereo Vision Michael Bleyer LVA Stereo Vision What Happened Last Time? Human 3D perception (3D cinema) Computational stereo Intuitive explanation of what is meant by disparity Stereo matching

### An Approach for Reduction of Rain Streaks from a Single Image

An Approach for Reduction of Rain Streaks from a Single Image Vijayakumar Majjagi 1, Netravati U M 2 1 4 th Semester, M. Tech, Digital Electronics, Department of Electronics and Communication G M Institute

### Topics in Computer Animation

Topics in Computer Animation Animation Techniques Artist Driven animation The artist draws some frames (keyframing) Usually in 2D The computer generates intermediate frames using interpolation The old

### Browsing: Query Refinement and Video Synopsis

April 23, 2009 CuZero: Frontier of Interactive Visual Search Goals Finding interesting content in video and images Solution Better video and image search Video synopsis to quickly scan long video Current

### Schedule. Tuesday, May 10: Thursday, May 12: Motion microscopy, separating shading and paint min. student project presentations, projects due.

Schedule Tuesday, May 10: Motion microscopy, separating shading and paint Thursday, May 12: 5-10 min. student project presentations, projects due. Computer vision for photography Bill Freeman Computer

### Online Interactive 4D Character Animation

Online Interactive 4D Character Animation Marco Volino, Peng Huang and Adrian Hilton Web3D 2015 Outline 4D Performance Capture - 3D Reconstruction, Alignment, Texture Maps Animation - Parametric Motion

### The Muvipix.com Guide to Vegas Movie Studio Platinum 14

The Muvipix.com Guide to Vegas Movie Studio Platinum 14 What have I gotten myself into?... 1 Some basic questions and simple answers about Vegas Movie Studio 14 and how it works Chapter 1 Get to know Vegas

### Multimedia Technology CHAPTER 4. Video and Animation

CHAPTER 4 Video and Animation - Both video and animation give us a sense of motion. They exploit some properties of human eye s ability of viewing pictures. - Motion video is the element of multimedia

### Graph Cuts vs. Level Sets. part I Basics of Graph Cuts

ECCV 2006 tutorial on Graph Cuts vs. Level Sets part I Basics of Graph Cuts Yuri Boykov University of Western Ontario Daniel Cremers University of Bonn Vladimir Kolmogorov University College London Graph

### Image Filtering, Warping and Sampling

Image Filtering, Warping and Sampling Connelly Barnes CS 4810 University of Virginia Acknowledgement: slides by Jason Lawrence, Misha Kazhdan, Allison Klein, Tom Funkhouser, Adam Finkelstein and David

### Analysis of Image and Video Using Color, Texture and Shape Features for Object Identification

IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. VI (Nov Dec. 2014), PP 29-33 Analysis of Image and Video Using Color, Texture and Shape Features

### Stop-Motion TOPICS. The Stop-Motion Camera Stop-Motion Options Add Mode Stop-Motion Camera Orientation Onion Skin

Stop-Motion Stop-Motion is a technique whereby objects are photographed in a series of slightly different positions such that they appear to move when the photographs are played back in quick succession.

### Computational Photography

End of Semester is the last lecture of new material Quiz on Friday 4/30 Sample problems are posted on website Computational Photography Final Project Presentations Wednesday May 12 1-5pm, CII 4040 Attendance

### CS 4495 Computer Vision A. Bobick. Motion and Optic Flow. Stereo Matching

Stereo Matching Fundamental matrix Let p be a point in left image, p in right image l l Epipolar relation p maps to epipolar line l p maps to epipolar line l p p Epipolar mapping described by a 3x3 matrix

### Review for the Final

Review for the Final CS 635 Review (Topics Covered) Image Compression Lossless Coding Compression Huffman Interpixel RLE Lossy Quantization Discrete Cosine Transform JPEG CS 635 Review (Topics Covered)

### AUTOMATIC 3D HUMAN ACTION RECOGNITION Ajmal Mian Associate Professor Computer Science & Software Engineering

AUTOMATIC 3D HUMAN ACTION RECOGNITION Ajmal Mian Associate Professor Computer Science & Software Engineering www.csse.uwa.edu.au/~ajmal/ Overview Aim of automatic human action recognition Applications

### CSE452 Computer Graphics

CSE452 Computer Graphics Lecture 19: From Morphing To Animation Capturing and Animating Skin Deformation in Human Motion, Park and Hodgins, SIGGRAPH 2006 CSE452 Lecture 19: From Morphing to Animation 1

### Image Warping and Mosacing

Image Warping and Mosacing 15-463: Rendering and Image Processing Alexei Efros with a lot of slides stolen from Steve Seitz and Rick Szeliski Today Mosacs Image Warping Homographies Programming Assignment

### Computational Foundations of Cognitive Science

Computational Foundations of Cognitive Science Lecture 16: Models of Object Recognition Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk February 23, 2010 Frank Keller Computational

### Object Removal Using Exemplar-Based Inpainting

CS766 Prof. Dyer Object Removal Using Exemplar-Based Inpainting Ye Hong University of Wisconsin-Madison Fall, 2004 Abstract Two commonly used approaches to fill the gaps after objects are removed from

### Sketch-based Interface for Crowd Animation

Sketch-based Interface for Crowd Animation Masaki Oshita 1, Yusuke Ogiwara 1 1 Kyushu Institute of Technology 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan oshita@ces.kyutech.ac.p ogiwara@cg.ces.kyutech.ac.p

### Crowd Scene Understanding with Coherent Recurrent Neural Networks

Crowd Scene Understanding with Coherent Recurrent Neural Networks Hang Su, Yinpeng Dong, Jun Zhu May 22, 2016 Hang Su, Yinpeng Dong, Jun Zhu IJCAI 2016 May 22, 2016 1 / 26 Outline 1 Introduction 2 LSTM

### Accurate 3D Face and Body Modeling from a Single Fixed Kinect

Accurate 3D Face and Body Modeling from a Single Fixed Kinect Ruizhe Wang*, Matthias Hernandez*, Jongmoo Choi, Gérard Medioni Computer Vision Lab, IRIS University of Southern California Abstract In this

### Quaternions and Rotations

CSCI 420 Computer Graphics Lecture 20 and Rotations Rotations Motion Capture [Angel Ch. 3.14] Rotations Very important in computer animation and robotics Joint angles, rigid body orientations, camera parameters

### Image Segmentation. Schedule. Jesus J Caban 11/2/10. Monday: Today: Image Segmentation Topic : Matting ( P. Bindu ) Assignment #3 distributed

Image Segmentation Jesus J Caban Today: Schedule Image Segmentation Topic : Matting ( P. Bindu ) Assignment #3 distributed Monday: Revised proposal due Topic: Image Warping ( K. Martinez ) Topic: Image

### Image warping and stitching

Image warping and stitching May 5 th, 2015 Yong Jae Lee UC Davis PS2 due next Friday Announcements 2 Last time Interactive segmentation Feature-based alignment 2D transformations Affine fit RANSAC 3 Alignment

### CAP 5415 Computer Vision. Fall 2011

CAP 5415 Computer Vision Fall 2011 General Instructor: Dr. Mubarak Shah Email: shah@eecs.ucf.edu Office: 247-F HEC Course Class Time Tuesdays, Thursdays 12 Noon to 1:15PM 383 ENGR Office hours Tuesdays

### Quaternions and Rotations

CSCI 480 Computer Graphics Lecture 20 and Rotations April 6, 2011 Jernej Barbic Rotations Motion Capture [Ch. 4.12] University of Southern California http://www-bcf.usc.edu/~jbarbic/cs480-s11/ 1 Rotations

### Image features. Image Features

Image features Image features, such as edges and interest points, provide rich information on the image content. They correspond to local regions in the image and are fundamental in many applications in

### Data-Driven Face Modeling and Animation

1. Research Team Data-Driven Face Modeling and Animation Project Leader: Post Doc(s): Graduate Students: Undergraduate Students: Prof. Ulrich Neumann, IMSC and Computer Science John P. Lewis Zhigang Deng,

### Spatial Control in Neural Style Transfer

Spatial Control in Neural Style Transfer Tom Henighan Stanford Physics henighan@stanford.edu Abstract Recent studies have shown that convolutional neural networks (convnets) can be used to transfer style

### Online Video Registration of Dynamic Scenes using Frame Prediction

Online Video Registration of Dynamic Scenes using Frame Prediction Alex Rav-Acha Yael Pritch Shmuel Peleg School of Computer Science and Engineering The Hebrew University of Jerusalem 91904 Jerusalem,

### Shift-Map Image Editing

Shift-Map Image Editing Yael Pritch Eitam Kav-Venaki Shmuel Peleg School of Computer Science and Engineering The Hebrew University of Jerusalem 91904 Jerusalem, Israel Abstract Geometric rearrangement

### Analysis and Synthesis of 3D Shape Families via Deep Learned Generative Models of Surfaces

Analysis and Synthesis of 3D Shape Families via Deep Learned Generative Models of Surfaces Haibin Huang, Evangelos Kalogerakis, Benjamin Marlin University of Massachusetts Amherst Given an input 3D shape

### Milestone Systems. XProtect Web Client 2018 R1. User Manual

Milestone Systems XProtect Web Client 2018 R1 User Manual Contents XProtect Web Client... 5 Log in... 6 Navigating the Home page (explained)... 7 Find a camera... 8 Search for a camera... 8 Navigate to

### Identifying Car Model from Photographs

Identifying Car Model from Photographs Fine grained Classification using 3D Reconstruction and 3D Shape Registration Xinheng Li davidxli@stanford.edu Abstract Fine grained classification from photographs

### CS448f: Image Processing For Photography and Vision. Fast Filtering

CS448f: Image Processing For Photography and Vision Fast Filtering Problems in Computer Vision Computer Vision in One Slide 1) Extract some features from some images 2) Use these to formulate some (hopefully

### Using Machine Learning to Optimize Storage Systems

Using Machine Learning to Optimize Storage Systems Dr. Kiran Gunnam 1 Outline 1. Overview 2. Building Flash Models using Logistic Regression. 3. Storage Object classification 4. Storage Allocation recommendation

### Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences

Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences Robert Pless Department of Computer Science and Engineering, Washington University in St. Louis, pless@cse.wustl.edu Abstract

### 2017 R3. Milestone Systems. XProtect Web Client 2017 R3. User Manual

2017 R3 Milestone Systems XProtect Web Client 2017 R3 User Manual Contents XProtect Web Client... 5 Log in... 5 Navigating the Home page (explained)... 5 Find a camera... 7 Search for a camera... 7 Navigate

About MPEG Compression HD video requires significantly more data than SD video. A single HD video frame can require up to six times more data than an SD frame. To record such large images with such a low

### Video Completion by Motion Field Transfer

Video Completion by Motion Field Transfer Takaaki Shiratori Yasuyuki Matsushita Sing Bing Kang Xiaoou Tang The University of Tokyo Tokyo, Japan siratori@cvl.iis.u-tokyo.ac.jp Microsoft Research Asia Beijing,

### Towards a Simulation Driven Stereo Vision System

Towards a Simulation Driven Stereo Vision System Martin Peris Cyberdyne Inc., Japan Email: martin peris@cyberdyne.jp Sara Martull University of Tsukuba, Japan Email: info@martull.com Atsuto Maki Toshiba

### Adaptive Multi-Stage 2D Image Motion Field Estimation

Adaptive Multi-Stage 2D Image Motion Field Estimation Ulrich Neumann and Suya You Computer Science Department Integrated Media Systems Center University of Southern California, CA 90089-0781 ABSRAC his

### Visual Recognition: Image Formation

Visual Recognition: Image Formation Raquel Urtasun TTI Chicago Jan 5, 2012 Raquel Urtasun (TTI-C) Visual Recognition Jan 5, 2012 1 / 61 Today s lecture... Fundamentals of image formation You should know

### Learning realistic human actions from movies

Learning realistic human actions from movies Ivan Laptev, Marcin Marszalek, Cordelia Schmid, Benjamin Rozenfeld CVPR 2008 Presented by Piotr Mirowski Courant Institute, NYU Advanced Vision class, November

### Motion Estimation. There are three main types (or applications) of motion estimation:

Members: D91922016 朱威達 R93922010 林聖凱 R93922044 謝俊瑋 Motion Estimation There are three main types (or applications) of motion estimation: Parametric motion (image alignment) The main idea of parametric motion

### Discrete Optimization Methods in Computer Vision CSE 6389 Slides by: Boykov Modified and Presented by: Mostafa Parchami Basic overview of graph cuts

Discrete Optimization Methods in Computer Vision CSE 6389 Slides by: Boykov Modified and Presented by: Mostafa Parchami Basic overview of graph cuts [Yuri Boykov, Olga Veksler, Ramin Zabih, Fast Approximation

### Non-Photorealistic Rendering

15-462 Computer Graphics I Lecture 22 Non-Photorealistic Rendering November 18, 2003 Doug James Carnegie Mellon University http://www.cs.cmu.edu/~djames/15-462/fall03 Pen-and-Ink Illustrations Painterly