Visual Perception

PhD Program in Information Technologies

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

Camera coordinate system

Worldcoordinatesystem

0 0,u v

fCY

CX CZ

CO

WZ

WY WX

WO

wP

Image plane

W

C

uP

IYIXIO I

RY

RX

R

RO

Imagecoordinatesystem

Step 4

dP

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) QuimCamera modelling, camera calibration: intrinsic and extrinsicparameters, stereo vision, epipolar geometry, fundamental matrix. Example: robot localization and 3D mapping.

3. Motion estimation. (4 hours) RafaTrinocular stereovision. Deriving homographies from the projection matrix. Robust estimators. Aplications: motion estimation through mosaicking. Derivation of extrinsic parameters.

Contents of the Course

4. The correspondence problem. (2 hours) RafaDetection of interest points. Finding correspondences. Similarity measurements. Aplying epipolar geometry.

5. 3D reconstruction using laser range finders. (2 hours) PepLaser beam calibration. Subpixel slit detection. Scanning. 3Dreconstruction. Examples. 6. Structured light (2 hours) QuimPattern projection. Pattern coding. Time multiplexing. Spatialneighborhood. Direct codification. Designing and Implementing an optimal pattern.

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

Contents of the Course

Schedule of the course

March 2004 April 2004

May 2004

Lesson Days

Practical Issues presentationSecond week of June

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

Image acquisition

Image interpretation

Introduction to Visual Perception

The Human Eye ?

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.

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.

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

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 controlRapid PrototypingComputer assisted surgeryFilm making effectsObject pickingRobot Navigation

Image acquisition

Image interpretation

INTERPRETATIONKNOWLEDGE

BASEDATABASE

InterpretationLevel

SCENEDESCRIPTIONFUSION TRACKING

SHAPEIDENTIFICATION

LOCALIZATIONSCENEANALYSIS

SEGMENTATION

3DINFORMATION

FEATUREEXTRACTION

MOVEMENTDETECTION

TEXTUREANALYSIS

IMAGERESTORATION

EDGERESTORATION

DescriptionLevel

Image ProcessingHigh Level

FILTERINGEDGE

THINNING

EDGEDETECTION

COLOURCOMPENSATION

THRESHOLDING

GRADIENTS

RE-HISTOGRAMATION

A/D

COLOURSEPARATION

SENSOR

Image AcquisitionLevel

Image ProcessingLow Level

3D Information

System selection

Modelling

Calibration

Correspondence

Get 3D Cloud

Data Fusion

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.

Main processes in 3D digitization

objectRange sensing

Geometric fusion

Objectmodeling

Texturemapping

Sensorplanning

N 3D pointclouds

solid(triangles)

solid(splines)

coloured solid

best nextview

graphicsurface

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

System Selection

24 aligned 3D scansready for merging

24 meshes merged into a surface triangulation.

set of six 3D scans acquired from different viewpoints and their alignment (center)

Geometric fusion