HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication.

Slides:

Advertisements

Similar presentations

Copyright© 2011, Intel Corporation. All rights reserved. *Other brands and names are the property of their respective owners. Intel ® Software Development.

Advertisements

Streaming SIMD Extension (SSE)

Intel Pentium 4 ENCM Jonathan Bienert Tyson Marchuk.

MP3 Optimization Exploiting Processor Architecture and Using Better Algorithms Mancia Anguita Universidad de Granada J. Manuel Martinez – Lechado Vitelcom.

ECE291 Computer Engineering II Lecture 24 Josh Potts University of Illinois at Urbana- Champaign.

Fall EE 333 Lillevik 333f06-l20 University of Portland School of Engineering Computer Organization Lecture 20 Pipelining: “bucket brigade” MIPS.

Stereo Many slides adapted from Steve Seitz. Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Where does the.

 Understanding the Sources of Inefficiency in General-Purpose Chips.

Department of Computer Science University of the West Indies.

Fall 2011SYSC 5704: Elements of Computer Systems 1 SYSC 5704 Elements of Computer Systems Optimization to take advantage of hardware.

Computer Vision Lab Contour Matching Using Epipolar Geometry (PAMI, April 2000) Young Ki Baik.

Software performance enhancement using multithreading and architectural considerations Prepared by: Andrey Sloutsman Evgeny Gokhfeld 06/2006.

Data Parallel Algorithms Presented By: M.Mohsin Butt

Computer Vision : CISC 4/689 Adaptation from: Prof. James M. Rehg, G.Tech.

Addressing Optimization for Loop Execution Targeting DSP with Auto-Increment/Decrement Architecture Wei-Kai Cheng Youn-Long Lin* Computer & Communications.

100+ Times Faster Weighted Median Filter [cvpr ‘14]

Stereo Sebastian Thrun, Gary Bradski, Daniel Russakoff Stanford CS223B Computer Vision (with slides by James Rehg and.

Visualization- Determining Depth From Stereo Saurav Basu BITS Pilani 2002.

Stereo matching “Stereo matching” is the correspondence problem –For a point in Image #1, where is the corresponding point in Image #2? C1C1 C2C2 ? ? C1C1.

Intel Pentium 4 Processor Presented by Presented by Steve Kelley Steve Kelley Zhijian Lu Zhijian Lu.

K-Ary Search on Modern Processors Fakultät Informatik, Institut Systemarchitektur, Professur Datenbanken Benjamin Schlegel, Rainer Gemulla, Wolfgang Lehner.

Prepared by: Hind J. Zourob Heba M. Matter Supervisor: Dr. Hatem El-Aydi Faculty Of Engineering Communications & Control Engineering.

Factor Graphs Young Ki Baik Computer Vision Lab. Seoul National University.

Real time DSP Professors: Eng. Julian Bruno Eng. Mariano Llamedo Soria.

Modularizing B+-trees: Three-Level B+-trees Work Fine Shigero Sasaki* and Takuya Araki NEC Corporation * currently with 1st Nexpire Inc.

Simultaneous Multithreading: Maximizing On-Chip Parallelism Presented By: Daron Shrode Shey Liggett.

History of Microprocessor MPIntroductionData BusAddress Bus

© 2007 SET Associates Corporation SAR Processing Performance on Cell Processor and Xeon Mark Backues, SET Corporation Uttam Majumder, AFRL/RYAS.

Speed-up of the ring recognition algorithm Semeon Lebedev GSI, Darmstadt, Germany and LIT JINR, Dubna, Russia Gennady Ososkov LIT JINR, Dubna, Russia.

Computer Vision Lab Seoul National University Keyframe-Based Real-Time Camera Tracking Young Ki BAIK Vision seminar : Mar Computer Vision Lab.

Stereo Many slides adapted from Steve Seitz.

Stereo Many slides adapted from Steve Seitz. Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image image 1image.

CS332 Visual Processing Department of Computer Science Wellesley College Binocular Stereo Vision Region-based stereo matching algorithms Properties of.

A Region Based Stereo Matching Algorithm Using Cooperative Optimization Zeng-Fu Wang, Zhi-Gang Zheng University of Science and Technology of China Computer.

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication.

Computer Vision Stereo Vision. Bahadir K. Gunturk2 Pinhole Camera.

Bahadir K. Gunturk1 Phase Correlation Bahadir K. Gunturk2 Phase Correlation Take cross correlation Take inverse Fourier transform  Location of the impulse.

Lecture 6. VFP & NEON in ARM

December 2, 2015Single-Instruction Multiple Data (SIMD)1 Performance Optimization, cont. How do we fix performance problems?

Introduction to MMX, XMM, SSE and SSE2 Technology

Fast Census Transform-based Stereo Algorithm using SSE2

Introdution to SSE or How to put your algorithms on steroids! Christian Kerl

GPU Based Sound Simulation and Visualization Torbjorn Loken, Torbjorn Loken, Sergiu M. Dascalu, and Frederick C Harris, Jr. Department of Computer Science.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

Computer performance issues* Pipelines, Parallelism. Process and Threads.

Playstation2 Architecture Architecture Hardware Design.

Visual Odometry David Nister, CVPR 2004

Computer Vision Group Department of Computer Science University of Illinois at Urbana-Champaign.

Media Processor Lab. Media Processor Lab. Trellis-based Parallel Stereo Matching Media Processor Lab. Sejong univ.

CUDA Compute Unified Device Architecture. Agent Based Modeling in CUDA Implementation of basic agent based modeling on the GPU using the CUDA framework.

SIMD Implementation of Discrete Wavelet Transform Jake Adriaens Diana Palsetia.

Capstone Design Implementation of Depth Sensor Based on Structured Infrared Patterns June 11, 2013 School of Information and Communication Engineering,

TEMPLATE DESIGN © H. Che 2, E. D’Azevedo 1, M. Sekachev 3, K. Wong 3 1 Oak Ridge National Laboratory, 2 Chinese University.

1 ECE 734 Final Project Presentation Fall 2000 By Manoj Geo Varghese MMX Technology: An Optimization Outlook.

Exploiting Graphics Processors for High-performance IP Lookup in Software Routers Jin Zhao, Xinya Zhang, Xin Wang, Yangdong Deng, Xiaoming Fu IEEE INFOCOM.

Stencil-based Discrete Gradient Transform Using

제 5 장 스테레오.

Exploiting Parallelism

Alvaro Mauricio Peña Dariusz Niworowski Frank Rodriguez

MMX Multi Media eXtensions

Special Instructions for Graphics and Multi-Media

STUDY AND IMPLEMENTATION

Binocular Stereo Vision

The Fast Curvelet Transform

Computer Vision Stereo Vision.

Mapping DSP algorithms to a general purpose out-of-order processor

Binocular Stereo Vision

Chapter 11: Stereopsis Stereopsis: Fusing the pictures taken by two cameras and exploiting the difference (or disparity) between them to obtain the depth.

Binocular Stereo Vision

Presentation transcript:

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering A Fast Cooperative Stereo Algorithm using 3D Moving Window and Parallel Processing Technique Young Ki Baik, Hyun Mok Cho°, Kyoung Mu Lee Visual Information Processing Lab. Hong-Ik Univ. Visual Information Processing Lab. School of Radio Science & Communication Engineering. Hong-Ik University

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Contents Introduction Introduction  Stereo vision  Cooperative stereo algorithm Fast CS algorithm Fast CS algorithm  Moving window technique  Parallel processing and optimization technique Result Result Conclusion & Future works Conclusion & Future works

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Stereo vision Extraction of the depth information from two or more images Extraction of the depth information from two or more images f (x, y, z) M N LR xlxl CrCr ClCl xrxr Focal length Scene object point b P PlPl PrPr

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Cooperative Stereo algorithm C.S Algorithm C.S Algorithm  Idea (Marr & Poggio, 1976) UniquenessUniqueness ContinuityContinuity  Embodiment (Zitnick and Kanade, 1999) Local support AreaLocal support Area Inhibition AreaInhibition Area

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Cooperative Stereo algorithm Illustration of 3D disparity space Illustration of 3D disparity space

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Cooperative Stereo algorithm Summary of algorithm Summary of algorithm  Prepare a 3D array ( memory allocation )  Set initial match values C 0  Iteratively update match values C n  Search maximum match value d

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Cooperative Stereo algorithm Initial Value Initial Value

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Cooperative Stereo algorithm 3D local support area 3D local support area Inhibition Function Inhibition Function Update Function Update Function

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (2D) Moving Window Technique (2D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (2D) Moving Window Technique (2D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (2D) Moving Window Technique (2D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (2D) Moving Window Technique (2D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (3D) Moving Window Technique (3D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Moving Window Technique (3D) Moving Window Technique (3D)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Parallel processing Technique SIMD (Single Instruction Multiple Data) SIMD (Single Instruction Multiple Data)  SSE2 (Streaming SIMD Extensions2) 128-bit SIMD integer arithmetic operations.128-bit SIMD integer arithmetic operations. 128-bit SIMD double precision floating point operations128-bit SIMD double precision floating point operations Cache and memory management operationsCache and memory management operations Data type and XMM Register

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Parallel processing Technique d direction redundancy d direction redundancy  Multiple calculation 4 operation at the same time4 operation at the same time (floating point operations) d

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Environment Environment  System : Pentium IV 1.4Ghz  Cache Memory : 256Kbyte Condition Condition  Image : 384x288 Tsukuba  Disparity searching range : 16  Initial window size : 3x3  Iteration : 1

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Fast CS Algorithm Time check Time check

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Result Local support window : 3x3x3 Local support window : 3x3x3 Local support window : 5x5x3 Local support window : 5x5x3 Original CS Algorithm Moving Window technique Parallel Processing Support region2.100 (sec)0.392 (sec)0.063 (sec) Inhibition region And update (sec)0.133 (sec)0.052 (sec) Sum (sec)0.525 (sec)0.115 (sec) Original CS Algorithm Moving Window technique Parallel Processing Support region5.582 (sec)0.395 (sec)0.064 (sec) Inhibition region And update (sec)0.134 (sec)0.052 (sec) Sum (sec)0.529 (sec)0.116 (sec)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Result Local support window : 7x7x3 Local support window : 7x7x3 Original CS Algorithm Moving Window technique Parallel Processing Support region (sec)0.395 (sec)0.064 (sec) Inhibition region And update (sec)0.134 (sec)0.052 (sec) Sum (sec)0.529 (sec)0.116 (sec)

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Result Time comparison Time comparison

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Result Tsukuba ImageTrue disparity map Initial disparity map 100 iteration result

HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication Engineering Conclusion & Future works Conclusion Conclusion  Fast Cooperative Stereo Algorithm 3D Moving Window Technique3D Moving Window Technique Parallel Processing TechniqueParallel Processing Technique Future works Future works  Removing redundancy for iteration  Calculating Optimization  Usage of SIMD technique  Application to other stereo algorithm