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June 21, 2022 The COSPAL Project 1 Computer Vision Laboratory The COSPAL Project Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN
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Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

Jan 18, 2016

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Page 1: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 1

Computer Vision Laboratory

The COSPAL Project

Michael FelsbergComputer Vision Laboratory

Linköping UniversitySWEDEN

Page 2: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 2

Computer Vision Laboratory

Fact Sheet

• COSPAL acronym:COgnitiveSystems using Perception-Action Learning

• STREP IST-2003-004176

• Project Start:1. July 2004

Page 3: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 3

Computer Vision Laboratory

ConsortiumLinköping University, SwedenComputer Vision Laboratory (coordinator)Gösta GranlundChristian-Albrechts-University of Kiel, GermanyCognitive SystemsGerald SommerUniversity of Surrey, UKCentre for Vision, Speech, & Signal ProcessingVaclav HlavacCzech Technical University in Prague, Czech RepublicCenter for Machine PerceptionJosef Kittler

Page 4: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

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Computer Vision Laboratory

Consortium

Page 5: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 5

Computer Vision Laboratory

Objectives

• Architectures for autonomous robots with cognitive capabilities

• Vision based systems

• Stationary manipulator

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Page 6: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

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Computer Vision Laboratory

Feature input Output

Scenedescription

Actiongeneration

Approach

• Action precedes perception • Incremental learning and exploration• Bidirectional interface to reasoning unit

Page 7: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 7

Computer Vision Laboratory

Early Demonstrator

• Parallel progress in WPs

• Concentrate on architectural aspect

• Fully automatic learning and evaluation

QuickTime™ and a decompressor

are needed to see this picture.

Page 8: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 8

Computer Vision Laboratory

Final Demonstrator I

• Embedding into ‘real’ world

• Scalability of the architecture

• Stability and robustness

Page 9: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 9

Computer Vision Laboratory

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Final Demonstrator II

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Page 10: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 10

Computer Vision Laboratory

Just a toy example?

Fact: this scenario can ‘easily’ be solvedby a traditional system design.

• Is it robust?

• Can it generalize?

• Is it scalable?

Page 11: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 11

Computer Vision Laboratory

Robustness/Generalization

?

Page 12: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 12

Computer Vision Laboratory

Challenge of COSPAL

… is NOT to solve the puzzle problem

… is NOT to do model registration and fitting

… but to develop a system architecture whichis able to semi-autonomously,step-by-step build up knowledgeand solution strategies

Page 13: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 13

Computer Vision Laboratory

Architecture

• Homogeneous network structures• Localized representations (channels)• Associations between channels to be learned

Page 14: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 15

Computer Vision Laboratory

Hardware

• PCs• Firewire cameras• RX90• 3D turntable

Page 15: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

April 21, 2023 The COSPAL Project 16

Computer Vision Laboratory

Software & Interfaces

• Heterogeneous code modules:– C++– Matlab– Python

• Loosely coupled processes– Controlling process– XML files– Exception: servoing (PVM)

Page 16: Computer Vision Laboratory December 20, 2015The COSPAL Project1 Michael Felsberg Computer Vision Laboratory Linköping University SWEDEN.

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Computer Vision Laboratory

Summary

• Autonomous cognitive system– Action-precedes-perception learning– Bidirectional interface to symbolic unit– Incremental learning at all levels

• Demonstrator as proof of architecture

• Architectural building blocks for systems in more serious applications