VBPR 1st seminar

Vision-Based Place Recognition for Autonomous RobotsFirst Seminar - Project Overview

Team Members:Ahmed Abd-El Fattah Mohammed

Ahmed Saher Maher

Mourad Aly Mourad

Yasser Hassan Ahmed

11Saturday, December 11, 2010

Prof.Dr Mohamed RoushdyDr. Mohamed Abdel MegeedDr. Safaa AminT.A. Mohamed FathySupervisors


Methodology

Improve previous work

Adaptive Multi-Scale Classification

Challenges

Testing Platform

Development Tools

AgendaWhat How

When

3

Objective

Theoretical Background

Motivation

Problem Definition

System Architecture

Conventional Pattern Recognition System Architecture

Related Work

ImageCLEF

Top Related Systems

Time Plan

Our Progress

Next Objective

References


Objective

Where am I ?



Computer Science

Where are we in the field of computer science ?

5

Computer Vision

Image Processing

Pattern Recognition

Artificial Intelligence


Motivation

Interested in robot vision.

Has many applications, help in rescue missions.

6

Co-operation between our university and Bielefeld University.


Problem Definition

What does it mean ?

Meeting Room

Vision-Based Place Recognition for Autonomous Robot


Problem DefinitionSLAM

Simultaneous Localization And Mapping.

Our problem is to focus on localization issues in most SLAM systems.


Conventional PR System Architecture

9

Training Phase Testing Phase

Sensing

Pre-Processing

Feature Extraction

Training

Knowledge Base

Sensing

Pre-Processing

Feature Extraction

Classification

Decision


Related WorkImageCLEF

ImageCLEF 201022-23th September 2010

A yearly contest which focuses on information retrieval using image processing. It branches to many applications

including robot vision.


Related Work

Olivier Saurer, Friedrich Fraundorfer, and Marc Pollefeys – “Visual localization using global visual

features and vanishing points” - ETH Zurich, Switzerland

1st PositionCVG

Pros Cons

Focused on feature extraction phase developed new feature

extraction algorithms.

Used very primitive classification methods.


Related Work

W.Lucetti, E. Luchetti – “Combination of Classifiers for Indoor Room Recognition” - Gustavo Stefanini

Research Center - Padua, 23 September 2010

4th PositionCentro Gustavo Stefanini

Pros Cons

Focused on classification phase developed many new

combination of classifiers.

Used very primitive feature extraction methods.


Methodology



Challenges

Testing Platform

Development Tools

AgendaWhat How

When

13

Objective


Motivation

Problem Definition

System Architecture


Related Work

ImageCLEF

Top Related Systems

Time Plan

Our Progress

Next Objective

References


Methodology1. Improve previous work

14

Combine the pros of each group.

Try to avoid their mistakes and cons.


Methodology2. Adaptive Multi-Scale Classification

Differentiation using discriminative features only.

Env 1 Kitchen,Bathroom

White illuminationColor White

Env 2LivingRoom,Office

White illumination Color Blue

Env 3BedRoom,Corridor

Yellow illumination Color Brown

15

How can the system differentiate between environments?

What is the meaning of an environment?


Methodology2. Adaptive Multi-Scale Classification

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Environment Identifier

PR System for Env 1

(Kitchen,Bathroom)

PR System for Env 2

(Office,Library)

PR System for Env 3(Bedrooms)

DecisionStart Operating

Current Environment

Simple classification

Full-scale PR systems

Unrecognized Image


Challenges

Objects’ appearance varies due to

Cluttered background.

Difference in illumination.

Imaging conditions.

Recognition algorithms perform differently with different environments.

It’s difficult to find a solution that is both resource efficient and perform with high accuracy, due to the very limited resources of a mobile robot.


Testing Platforms

1) Bielefeld University’s workbench

2) ImageCLEF’s testing dataset.

3) Build our own data acquisition tool.


Development Tools

19

C++ Matlab


AgendaWhat How

When

20

Objective


Motivation

Problem Definition

System Architecture


Related Work

ImageCLEF

Top Related Systems

Methodology



Challenges

Testing Platform

Development Tools

Time Plan

Our Progress

Next Objective

References


Time Plan

21

201020102010 201120112011Sep Oct Nov Dec Jan Feb Mar April May June July

Feasibility Study

Survey(1)-Project Overview

Survey(2)-Project In Depth

Developing Simple PR System

Iterative System Development

Deployment

Documentation


Our Progress

22

Survey 1(Project Overview)

-Problem definition.-Commonly used algorithms in pattern recognition.

Survey 2(Project in Depth)

-Description of each algorithm mentioned in survey 1


Next ObjectiveSimple Pattern Recognition System

Simple PR System

ImageImageCLEF

Data Set

The system has the ability to differentiate between 2 classes.

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Decision ( Class 1 Or

Class 2 )


References

24

“The Robot Vision Track at ImageCLEF 2010”Andrzej Pronobis, Marco Fornoni, Henrik I. Christensen, and Barbara Caputo.

“Evaluation of Bayes, ICA, PCA and SVM Methods for Classification”, V.C.Chen. Radar Division, US Naval Research Laboratory.

Olivier Saurer, Friedrich Fraundorfer, and Marc Pollefeys – “Visual localization using global visual features and vanishing points” - ETH Zurich, Switzerland

W.Lucetti, E. Luchetti – “Combination of Classifiers for Indoor Room Recognition” - Gustavo Stefanini Research Center - Padua, 23 September 2010


Contacts

Blog: autovpr.wordpress.com

Ahmed Saher Maher

[email protected]

Ahmed Abd El-Fattah

[email protected]

Mourad Aly Mourad

[email protected]

Yasser Hassan Ahmed

[email protected] Thanks!25


mailto:[email protected]








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VBPR 1st seminar