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Visions%20of%20The%20Virtual%20Slaughterhouse

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Technical University of Denmark. Danish Meat Research Institute ... Collaboration with IMM, University of rhus and Visiana. Own CT-scanner. 4 phd-students ... – PowerPoint PPT presentation

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Title: Visions%20of%20The%20Virtual%20Slaughterhouse


1
Visions of The Virtual Slaughterhouse
  • Søren G. Erbou
  • Ph.D. student
  • Informatics and Mathematical Modelling
  • Technical University of Denmark
  • Danish Meat Research Institute
  • sge_at_imm.dtu.dk

2
Outline
  • What is the The Virtual Slaughterhouse?
  • Where are we now?
  • Visions for the future
  • Means
  • Applications
  • Summary

3
The Virtual Slaughterhouse
  • Part of the Danish Meat Research Institute
    (Roskilde, 1954)
  • Owned by the Danish pig producers via the Danish
    Meat Association
  • Mission of DMRI
  • Leading knowledge centre within meat and
    slaughter technology
  • The Virtual Slaughterhouse (2006-08)
  • 3D-models of pig carcasses and tools for
    analysing the models
  • Collaboration with IMM, University of Århus and
    Visiana
  • Own CT-scanner
  • 4 phd-students
  • Why?

4
Where are we now?
  • Pig slaughter automation programme (1998-gt)
  • gt30 development projects, gt40 mill.
  • Improved working environment, hygiene and safety
  • Examples

Cleaning of throat- and heart regions
Removal of inner bones from front part
5
Where are we now?
  • Highly automated production
  • Need for better quality control
  • Reverse problem in the production
  • Normal Many small parts assembled into one final
    product
  • Reverse Same input disassembled into many
    products
  • Large variability of input
  • Products restrict each other
  • Minimise variability on output and maximise
    profit
  • New methods for production planning
  • Operations research
  • Define new input features

6
Visions for the future
  • Where do we want to be in 10 years?
  • More adaptable production
  • Better suited for niche production
  • Less variability in quality of the final products
  • Handle large variability of input
  • Keep the leading technological position worldwide

7
Means
  • Modelling the biological variability
  • Modelling specific cuttings
  • New predictors of quality based on models
  • Optimise the cutting of each carcass separately
  • Introducing the CT-technology to the
    slaughterhouse industry
  • Image analysis is the key for extracting only the
    necessary information

8
Online CT
  • Adapt cutting to each specific carcass
  • Unlimited amount of information available at an
    early stage along the slaughterline
  • Extract useful information
  • Define predictors for quality
  • Speed and cost is crucial
  • Minimise data acquisition while maximising useful
    information

9
Applications
  • Trimming of the fat layer on the loin or neck
    muscle
  • The same fat thickness all over the loin gives
    the best price
  • Small thickness is better
  • Important not to expose the muscle
  • CT can give additional information
  • Model the fat layer
  • Fit the model to new carcasses

(University of Nebraska, Lincoln)
10
Applications
  • Use models for developing robotic tools
  • 40 carcasses CT-scanned
  • 3D statistical shape model of bone structures in
    the ham.
  • 7 model parameters describe 69 of the variation
  • Parameters are localised to ease interpretation

11
Even more applications
  • Segmentation of muscles
  • Cutting and quality estimation of pig backs
  • Pig atlas
  • Volume registration
  • Apply cuts in atlas and propagate to population
  • Virtual dissection
  • Voxelwise classification
  • Better quality measure
  • Many more to come

12
Summary
  • Obtaining and analysing 3D-models of pig
    carcasses
  • Introducing CT in the slaughterhouses
  • Image analysis and statistics are key elements
  • Predictors for quality
  • From models to robotic tools
  • Better suited for niche products
  • Production more adaptable
  • Quality control is crucial

13
Aknowledgements
  • DMRI
  • Eli V. Olsen
  • Lars B. Christensen
  • Claus Borggård
  • IMM-DTU
  • Martin Vester-Christensen
  • Mads F. Hansen
  • Peter S. Jørgensen
  • Allan Lyckegaard
  • Rasmus Larsen
  • Bjarne K. Ersbøll,
  • Thank you for your attention
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