fast census transform based stereo algorithm using sse2 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Fast Census Transform-based Stereo Algorithm using SSE2 PowerPoint Presentation
Download Presentation
Fast Census Transform-based Stereo Algorithm using SSE2

Loading in 2 Seconds...

play fullscreen
1 / 20

Fast Census Transform-based Stereo Algorithm using SSE2 - PowerPoint PPT Presentation


  • 835 Views
  • Uploaded on

Fast Census Transform-based Stereo Algorithm using SSE2. Young Ki Baik* Kyoung Mu Lee Computer Vision Lab. School of Electrical Engineering and Computer Science Seoul National University. Contents. Stereo Vision Census Transform Stereo Vision Fast approaches Experimental result

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Fast Census Transform-based Stereo Algorithm using SSE2' - long


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
fast census transform based stereo algorithm using sse2

Fast Census Transform-based Stereo Algorithm using SSE2

Young Ki Baik*

Kyoung Mu Lee

Computer Vision Lab.

School of Electrical Engineering and Computer Science

Seoul National University

contents
Contents
  • Stereo Vision
  • Census Transform Stereo Vision
  • Fast approaches
  • Experimental result
  • Conclusion and Future work
introduction
Introduction
  • What is the stereo vision?
    • The stereo vision is the method to extract 3D information using image from different view points.
      • Topographical survey
      • Obstacle detection
      • Object tracking
      • Face recognition
introduction4
Introduction
  • Trade off of algorithms
    • Algorithm for accurate results
      • Complex computation and iteration
      • Slow processing time
      • Unable to realize real-time system
    • Algorithm for fast processing time
      • Simple computation and no iteration
      • Fast processing time
      • Unable to realize accurate system
introduction5
Introduction
  • Fast stereo vision algorithm
    • Window size invariant method
      • Box filtering method
        • “Box-filtering techniques”, M.J.McDonnell (CGIP-81’)
        • “Real time correlation-based stereo : algorithm, implementations and applications”, Olivier Faugeras , Zhengyou Zhang , … (Tech.Rep.RR-2013, INRIA,1993)
    • Disparity range invariant method
      • Rectangular subregioning method
        • “Rectangular Subregioning and 3-D Maximum-Surface Techniques for Fast Stereo Matching”, Changming Sun (CVPR-2001)
    • Parallel processing technique
introduction6
Introduction
  • Problem
    • Real images from grabbers can not assure of brightness consistency in corresponding region.
    • Intensity correlation method is not proper for real images.
  • Census transform
    • Census transform has been evaluated as the method robust to radiometric distortion.
      • J. Banks and P. Corke, "Quantitative evaluation of matching methods and validity measures for stereo vision," Int. J. Robotics Research, vol. 20, pp. 512-532, July 2001.
      • Heiko Hirschmller, "Improvements in Real-Time Correlation Based Stereo Vision", Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision, pp. 141-148, Kauai, Hawaii, December 2001.
census transform stereo vision

121

130

26

31

39

1

1

0

0

0

109

115

33

40

30

1

1

0

0

0

98

102

78

67

45

1

1

X

0

0

47

67

32

170

198

0

0

0

1

1

39

86

99

159

210

1

1

1

1

1

Census transform window (CTW)

1

1

0

0

0

1

1

0

0

0

1

1

0

0

0

0

0

1

1

1

1

1

1

1

Census Transform Stereo Vision
  • Census transform
    • Census transform converts relative intensity difference to 0 or 1 and deforms 1 dimensional vector as much as window size of census transform.
census transform stereo vision8

Height

Width

Census Transform Stereo Vision
  • Result of census transform
    • Census transform makes data of (image size * vector size).

(Square size of CTW)-1

Height

Width

census transform stereo vision9
Census Transform Stereo Vision
  • Sum of Hamming distance
    • The Hamming distance of two transformed vectors with correlation windows is used to find corresponding region.

Sum of Hamming distance

Disparity range

Right census

transformed vector

3D disparity space

Left census

transformed vector

census transform stereo vision10
Census Transform Stereo Vision
  • Complexity of algorithm
    • Census transform-based stereo vision (CTSV) has high complexity.

N : Searching window size

D : Disparity range

C : Census transform window size

fast census transform stereo vision
Fast Census Transform Stereo Vision
  • Fast Census Transform Stereo Vision

Census transform

Hamming distance

Parallel processing

- SSE2

8bit look-up table

Parallel processing

- SSE2

Correlation

Moving window technique

Parallel processing

- SSE2

fast census transform stereo vision12
Fast Census Transform Stereo Vision
  • SSE2 (Streaming SIMD Extension 2)
    • 128-bit SIMD packed integer & floating point arithmetic operation
    • Cache and memory management operation
    • Continuous memory structure is required
    • No advantages in separate data
fast census transform stereo vision13
Fast Census Transform Stereo Vision
  • Fast approaches (census transform)
    • Usage of parallel processing (SSE2)
      • 16 pixels are loaded to XMM(SSE2 memory) and computed at once.
fast census transform stereo vision14
Fast Census Transform Stereo Vision
  • Fast approaches (sum of Hamming distance)
    • Usage of 8bit look-up table (LUT)
    • Parallel processing : SSE2
    • Parallel processing is faster than 8 bit LUT
fast census transform stereo vision15
Fast Census Transform Stereo Vision
  • Fast approaches (correlation)
    • Moving window technique
fast census transform stereo vision16
Fast Census Transform Stereo Vision
  • Fast approaches (correlation)
    • Combination of moving window and SIMD
      • Moving window technique for x, y-axis
      • SSE2 for d-axis
experimental result
Experimental result
  • Environment
    • System : Pentium-IV 2.4GHz
    • Cache memory : 512Kbyte
    • Camera : Stereo Mega-D (Videre design)
  • Condition
    • Image size : 320 x 240 gray stereo images
    • Census transform window size :5x5, 7x7, 9x9
    • Disparity searching range : 32
    • Correlation window size : 11x11
experimental result18
Experimental result
  • Detail processing time
    • 32 disparity searching range
experimental result19
Experimental result
  • Performance of census transform stereo vision
conclusion and future work
Conclusion and Future work
  • Conclusion
    • Moving window technique reduces processing time to constant except in transforming stage
    • SSE2 instructions reduces running time by 2.5 to 3 times
  • Possibility for faster result
    • Specialized Instruction
    • 16 bit look-up table
    • Fixed window size of census transform
  • Future work
    • Applying real-time approach to another stereo algorithm
    • Combine stereo system to other applications