Computer vision
Download
1 / 23

Computer Vision - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

Computer Vision. Stereo Vision. Pinhole Camera. Perspective Projection. Stereo Vision. Two cameras. Known camera positions. Recover depth. scene point. p. p’. image plane. optical center. Correspondences. p. p’. Matrix form of cross product. a =a x i +a y j +a z k.

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 ' Computer Vision ' - chanda-roberts


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
Computer vision

Computer Vision

Stereo Vision




Stereo vision
Stereo Vision

  • Two cameras.

  • Known camera positions.

  • Recover depth.

scene point

p

p’

image plane

optical center



Matrix form of cross product
Matrix form of cross product

a=axi+ayj+azk

a×b=|a||b|sin(η)u

b=bxi+byj+bzk


The essential matrix
The Essential Matrix

Essential matrix


Stereo constraints

Epipolar Line

p’

Y2

X2

Z2

O2

Epipole

Stereo Constraints

M

Image plane

Y1

p

O1

Z1

X1

Focal plane


A simple stereo system

disparity

Depth Z

Elevation Zw

A Simple Stereo System

LEFT CAMERA

RIGHT CAMERA

baseline

Right image:

target

Left image:

reference

Zw=0


Stereo view
Stereo View

Right View

Left View

Disparity


Stereo disparity
Stereo Disparity

  • The separation between two matching objects is called the stereo disparity.


Parallel cameras
Parallel Cameras

P

Z

xl

xr

f

pl

pr

Ol

Or

Disparity:

T

T is the stereo baseline



Correlation approach

(xl, yl)

Correlation Approach

LEFT IMAGE

  • For Each point (xl, yl) in the left image, define a window centered at the point


Correlation approach1
Correlation Approach

RIGHT IMAGE

(xl, yl)

  • … search its corresponding point within a search region in the right image


Correlation approach2
Correlation Approach

RIGHT IMAGE

(xr, yr)

dx

(xl, yl)

  • … the disparity (dx, dy) is the displacement when the correlation is maximum


?

=

g

f

Most

popular

Comparing Windows


Comparing Windows

Minimize

Sum of Squared

Differences

Maximize

Cross correlation


Correspondence difficulties
Correspondence Difficulties

  • Why is the correspondence problem difficult?

    • Some points in each image will have no corresponding points in the other image.

      (1) the cameras might have different fields of view.

      (2) due to occlusion.

  • A stereo system must be able to determine the image parts that should not be matched.


Structured light
Structured Light

  • Structured lighting

    • Feature-based methods are not applicable when the objects have smooth surfaces (i.e., sparse disparity maps make surface reconstruction difficult).

    • Patterns of light are projected onto the surface of objects, creating interesting points even in regions which would be otherwise smooth.

  • Finding and matching such points is simplified by knowing the geometry of the projected patterns.


Stereo results
Stereo results

  • Data from University of Tsukuba

Scene

Ground truth

(Seitz)


Results with window correlation
Results with window correlation

Estimated depth of field

(a fixed-size window)

Ground truth

(Seitz)


Results with better method
Results with better method

  • A state of the art method

    • Boykov et al., Fast Approximate Energy Minimization via Graph Cuts,

    • International Conference on Computer Vision, September 1999.

Ground truth

(Seitz)


ad