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PhD Program in Information Technologies. Visual Perception. Description : Obtention of 3D Information. Study of the problem of triangulation, camera calibration and stereovision. Passive and active vision. Epipolar geometry and bidimensional transformations. Coordinator : Dr. Rafael Garcia

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slide1

PhD Program in

Information Technologies

Visual Perception

Description:

Obtention of 3D Information. Study of the problem of triangulation, camera calibration and stereovision. Passive and active vision. Epipolar geometry and bidimensional transformations.

Coordinator: Dr. Rafael Garcia

Professors: Dr. Rafael Garcia “Rafa”, Dr. Joaquim Salvi “Quim”, Josep Forest “Pep”.

Term: March – April

Day & Time: Friday from 11 to 13 h.

Place: Seminari EIA

slide2

Contents of the Course

1. Introduction to visual perception (2 hours)

· Human vision. Image interpretation: brain vs. computer. Phases of image processing. Quim

· CCD sensors. Type of cameras: matricial, linear, 1 CCD, 3CDD, Analog, Digital. Rafa

2. Camera modelling and calibration (2 hours) Quim

Camera modelling, camera calibration: intrinsic and extrinsic

parameters, stereo vision, epipolar geometry, fundamental matrix. Example: robot localization and 3D mapping.

3. Motion estimation. (4 hours) Rafa

Trinocular stereovision. Deriving homographies from the projection matrix. Robust estimators. Aplications: motion estimation through mosaicking. Derivation of extrinsic parameters.

slide3

Contents of the Course

4. The correspondence problem. (2 hours) Rafa

Detection of interest points. Finding correspondences. Similarity measurements. Aplying epipolar geometry.

5. 3D reconstruction using laser range finders. (2 hours) Pep

Laser beam calibration. Subpixel slit detection. Scanning. 3D

reconstruction. Examples.

6. Structured light (2 hours) Quim

Pattern projection. Pattern coding. Time multiplexing. Spatial

neighborhood. Direct codification. Designing and Implementing an optimal pattern.

Practical issues: Modelization and calibration of a computer vision system and reconstruction of 3D objects.

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Schedule of the course

March 2004

April 2004

May 2004

Lesson Days

Practical Issues presentation

Second week of June

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Introduction to Visual Perception

  • Human Vision:
    • Identify objects
    • Determine the shape
    • Locate its 3D position.

Image acquisition

Image interpretation

slide6

The Human Eye ?

  • Eye shape:
    • Cornea: Transparent surface.
    • Sclera: Outer cover composed of a fibrous coat that surrounds the choroid.
    • Choroid: a layer of blood capillaries.
    • Retina: layer inside the choroid composed of two types of receptors (rods and cones) and a netword of nerves.
    • Optic nerve: Retinal nerves leave the eye to the brain trough the optic nerve bundle.
  • Image enhancement:
    • Cornea: Transparent surface.
    • Lens: Focuses the light to the retina surface to perform proper focus of near and distant objects.
    • Iris: Acts as a diaphragm to control the amount of light entering the eye.
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How an eye is working ?

  • Image acquisition:
    • Retina: Composed of
      • 100 M. Rods: Long slender receptors.
      • Sensitive at low levels of light.
      • 6.5 M. Cones. Shorter and thicker receptors.
      • Sensitive at high levels of light.
      • Greatest presence at the Fovea region (sharpest vision).
        • Three types of cones with different wavelength absorption with peaks in the blue, green and red light spectrum
    • Light stimulus activate a rod or cone producing a nerve impulse which is transmitted through the optic nerve.

More information at: http://www.vision.ca/eye/lobby.html

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Computer Vision:

    • Object Recognition. Object Localisation.
    • Advantage: Automatisation.
    • Constraint: Difficult to transmit the human intelligence and skills to a computer.
  • Applications:
    • Shape Inspection for quality control
    • Rapid Prototyping
    • Computer assisted surgery
    • Film making effects
    • Object picking
    • Robot Navigation

Image acquisition

Image interpretation

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3D Information

System selection

Modelling

Calibration

Correspondence

Get 3D Cloud

Data Fusion

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System Selection

  • Combination of computational and optical techniques aimed at estimating or making explicit geometric (3D shape) properties of objects or scenes from their digital images.
  • stereovision
  • pattern projection
  • laser scanning
  • shape from X (motion, texture, shading, focus, zoom)
  • Computation for all or some pixels of the distance between a known reference frame and the scene point that is imaged in those pixels. The output is a range image (depth map) or a cloud of points {(xi, yi, zi), i=1..N}.
  • The fusion of several range images or point clouds corresponding to partially different views of an object may yield its full 3D digitization.
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Main processes in 3D digitization

solid(triangles)

graphicsurface

N 3D point

clouds

Range sensing

Geometric fusion

Objectmodeling

object

solid

(splines)

System Selection

best nextview

Sensorplanning

  • Stereovision
  • Pattern projection
  • Laser scanning
  • Shape from X
  • (motion, texture, shading, focus, zoom)

Texturemapping

coloured solid

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Geometric fusion

24 aligned 3D scans

ready for merging

set of six 3D scans acquired from different

viewpoints and their alignment (center)

24 meshes merged into a

surface triangulation.