technion faculty of electrical engineering project a 044167 summer 2001 israel institute of...

Technion

Faculty of Electrical Engineering

Project A 044167

Summer 2001

Israel Institute of Technology

3D Geometric Objects Search• Project team: Lyakas Alexander 307666883

• Instructor: Dr. Sigal Ar

The Main Idea

• Given a collection (database) of objects• Choose a search object

• Find objects that are similar to the search object

• The search is iterative and interactive• A user marks some objects as ‘GOOD’ or ‘BAD’

• The search program tries to refine the search by considering the user’s feedback

The Main Idea – Cont.

An Example

An Example – Cont.

• The following components available from

Starting the Project

Iterative and Interactive Search for Objectsby Moty Golan & Oren Kerembased onSimilarity Between Three-Dimensional Objects –An Iterative and Interactive Approachby Michael Elad, Ayellet Tal, Sigal Ar.

• Two databases: 3D colorless models & 2D images• A search program

• Test the approach with 3D colored models

• Design & perform system tests with real users

Project Requirements

• Improve the search program

Project Requirements - Cont.

• Build a database of 3D colored models

• Gather 3D colored objects from the WWW

• Perform preprocessing calculations, i.e. present each object in a way that will enable searching


• Improve the search program

• Adding a new database must not influence the search program’s code

• Add features needed for testing


• Test the system with real users

• Design the tests

• Perform tests with volunteers

• Draw conclusions

Working with Objects

• Each object is presented as a numerical vector, AKA ‘feature vector’

• To calculate feature vectors we used moments of different orders on

• colored points in 3D

• colored normals in 3D

• colorless points in 3D

• colorless normals in 3D

Comparing Objects

),...,(),...,( 11 nn yyYxxX ),...,(),...,( 11 nn yyYxxX • Consider two objects represented as feature vectors:

n

iii yxYXD

1

2)(),(

n

iii yxYXD

1

2)(),(

• We can compare them using the (square of) standard Euclidean distance:

• By adding weights and a bias value we can refine the distance function:

byxwYXDn

iiii

1

2)(),( byxwYXDn

iiii

1

2)(),(

Data Preprocessing

• Convert the objects to the format convenient to be sampled

• Perform sampling

• Correct normals directions• Normalize rotation and scale

• Create icons for all objects

• Calculate features vectors

• Before sampling each object is presented as colored triangular mesh

Sampling

• The sampling workflow:• Choose a triangle to sample

• Sample a point, normal and color from the chosen triangle

• Do this as many times as needed (10,000 in our case)

Ensure uniformsampling

Calculating features

ri

N

i

qi

pipqr zyx

Nm

1

1ˆ

ri

N

i

qi

pipqr zyx

Nm

1

1ˆ

• We approximate moments as:

• The pqr-th moment (of a 3D object) is defined as:

surface

rqppqr dxdydzzyxm

surface

rqppqr dxdydzzyxm

• The order of the moment is p+q+r

• Feature vector of level 3 in ‘colorless 3D’ look like:

...)ˆˆˆˆˆˆˆ( ,021,012,120,102,003,030,300 mmmmmmm ...)ˆˆˆˆˆˆˆ( ,021,012,120,102,003,030,300 mmmmmmm

The Search Program

• The extendibility requirement – adding new database must not influence the search program code• The object-based solution introduces the DBLINK class• Database-specific information is stored inside DBLINK objects only• One DBLINK object for each database – stored on disk

The Search Program – Cont.

Saving Test Sessions Results

• Automatic Screen Shooting• Before search refinements – with user’s ‘GOOD/BAD’ markings• When the new results are displayed

• Manual Screen Shooting

Testing the system

• Several volunteers that had no previous knowledge about how the system works• Tests were done for several test objects

• For each test object – all search configurations were tried

• The testers gave feedback on the search results

Test Example 1

Test Example 1 - Cont.

Test Example 2 - Cont.

Testing Results ExampleObject №TesterConfigurationFeedback

across iterations

Final feedback

595Shpitser Boris‘Pixels’, level 44 7 7 6 8

3 3 3 5 4

1 2 2 3 3

‘Results do not improve. Few good

results’.

595Brouk Aleksey‘Pixels’, level 41 4 4 9

1 4 8 9

2 4 8 8

‘Many good results’.

595Lyakas Igor‘Pixels’, level 41 9 9

1 7 9

2 6 9

‘Found everything I meant’.

890Shpitser Boris‘Colorless Pixels’, level 4

3 3 1 1 1 1

2 3 2 4 4 1

1 2 3 3 4 2

‘The search did not converge’.

890Brouk Aleksey‘Colorless Pixels’, level 4

1 10 1 3 10 10

2 2 3 3 5 7

1 2 3 4 4 5

‘Good results in first two rows’.

890Lyakas Igor‘Colorless Pixels’, level 4

2 8 1 1 8 8

2 2 3 1 4 4

2 3 3 3 4 4

‘Results do not change. Very few

good results’.

Conclusions

• No search configuration worked well for all objects

• ‘Normals’, level 4 worked good• but slow…

• In most cases the search converged• not always with good search results…

• So should the colors be considered?• …

• Searching for objects having a ‘family’ was successful with most configurations

The End

• See the project book for many skipped details

technion faculty of electrical engineering project a 044167 summer 2001 israel institute of...

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