generation of virtual image from multiple view point image database n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Generation of Virtual Image from Multiple View Point Image Database PowerPoint Presentation
Download Presentation
Generation of Virtual Image from Multiple View Point Image Database

Loading in 2 Seconds...

play fullscreen
1 / 19

Generation of Virtual Image from Multiple View Point Image Database - PowerPoint PPT Presentation


  • 323 Views
  • Uploaded on

・. Generation of Virtual Image from Multiple View Point Image Database . Haruki Kawanaka, Nobuaki Sado and Yuji Iwahori Nagoya Institute of Technology, Japan . Background.

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 'Generation of Virtual Image from Multiple View Point Image Database' - libitha


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
generation of virtual image from multiple view point image database

Generation of Virtual Image from Multiple View Point Image Database

Haruki Kawanaka, Nobuaki Sado and Yuji Iwahori

Nagoya Institute of Technology, Japan

background
Background
  • The soccer playing game has become popular in Japan since the World Cup Soccer of Japan and South Korea cosponsorship was held in 2002.
  • It is desired to see the game from various view points.
    • setting cameras at the reverse side of the goal
    • at the ceiling to view down.
    • many cameras are set at various locations
    • a camera has the function of pan−tilt−zoom

But, these are not free viewpoints.

previous method to generate a virtual image
Previous method to generate a virtual image

  • large scale environment with many camera settings and installations
    • It takes much cost.
    • the application is restricted at only that stadium.
  • using a few cameras and a motion capture
    • at only the indoor space
    • the special wear and several markers

It is difficult to use in actual games.

present approach
Present approach

  • The labels of the back number are generated as the virtual image.

The pose of each player is not considered.

study purpose
Study Purpose

  • generate a virtual image at another view point
    • from a real image
    • without the special environment
proposed method
Proposed Method
  • The appropriate pose image of each player is determined from using multiple viewpoint image database of a player’s CG model.
  • Each pose image is synthesized at the position to the virtual scene.
  • The position of each player is assumed to be provided by the trajectory system.
trajectory recording system

Trajectory of two players

Trajectory Recording System
  • We have developed Trajectory Recording System.

flow of proposed method
Flow of Proposed Method
  • Creation of database by CG model
  • Generation of virtual image for each player from image database
    • Recognize the pose of a player
    • Generate the corresponding virtual image from another view point
  • Synthesis of another view point image

creation of database by cg model images for database
Creation of database by CG model ~ Images for Database ~
  • Image Database (Human model) is created by CG.
    • Various motions (run, walk, shot, pass, heading, trap etc) 280 poses
    • From 8 view direction (rotation with every 45°)
    • total 2240 images

creation of database by cg model processing of each image
Creation of database by CG model ~ Processing of Each Image~

CG Image of human model

  • To eliminate of many factors such as the condition of light source, skin color, hair and uniform…
    • It is necessary to save the data size and the search time of the image database.
  • Image database is created using the silhouette.
    • This depends on the difference of pose but does not depend on such factor of each player.

Silhouette

creation of database by cg model processing of each image1

N

N

Creation of database by CG model ~ Processing of Each Image~

Normalize the image size

1-dimensional data

  • The image size for each pose is normalize.
    • The rectangle region which surrounds the silhouette of each pose, is extracted.
    • The extracted region just touches to the square with keeping its aspect ratio.
  • By the raster scan, one dimensional expansion of the normalized image is made.

[00011000・・・]

N×N

creation of database by cg model principal component analysis

[00011000・・・]

[01011010・・・]

・・・

[10001001・・・]

Creation of database by CG model ~ Principal Component Analysis~

Set of image data

(all poses & all view)

Image data

If the sum of eigen values becomes over 90%

effective

Compress & Projection onto

eigen space

Covariance matrix

eigen values,

eigen vectors

generation of virtual image for each player recognize the pose of a player 1
Generation of virtual image for each player ~ Recognize the pose of a player(1)~

Detection

of player

N

by eigen vector

1-D

[0000111・・・]

N

Projection onto eigen space

Normarize

Each silhouette is normalized, changed to one dimensional vector and projected to a point in the eigen space.

generation of virtual image for each player recognize the pose of a player 2
Generation of virtual image for each player ~ Recognize the pose of a player(2)~

minimum

distance

A

B

e3

given image

e2

e1

Result of search

CG image

Real image

Eigen Space

When Ais given, B is selected as the most similar sample. The pose of image A is recognized as B.

generation of virtual image generate the corresponding virtual image from another view point
Generation of virtual image ~ Generate the corresponding virtual image from another view point~

Another view image is made from result of search according to view point & view direction of virtual image.

Coordinates are acquired

from trajectory recording system

(x, y)

Generated virtual scene

Virtual stadium created using the OpenGL

experiments
Experiments

Actual original image

Virtual image from the same view direction as original

The experiment of pose recognition

experiments1
Experiments

Virtual image from different view point

Original image

Player’s position is fixed.

Viewpoint is moved.

Texture is used as soccer field.

It is also possible to generate an animation of movie by connecting each frame image sequentially.

conclusion
Conclusion
  • A new approach to generate a virtual image from another view point is proposed.
    • Multi-image database to apply the eigen space method for the pose recognition.
    • This approach is simple but generates the reasonable virtual scene.

future works
Future Works
  • It is difficult to discriminate the absolute pose of each player.
  • It is also difficult to treat the overlapped case in which two or more players cross.
  • Investigation of more effective matching approach is required to reduce the cost of time and memory.