Image Processing for Materials Characterization: Issues, Challenges - PowerPoint PPT Presentation

Motivation Material images Challenges and oportunities The special session Conclusions Image Processing for Materials Characterization: Issues, Challenges and Opportunities L. Duval 1 , 3 , M. Moreaud 1 , C. Couprie 1 , D. Jeulin 2 , H. Talbot 3 , J. Angulo 2 1 IFP Energies nouvelles 2 MINES ParisTech 3 Universit´ e Paris Est ICIP 2014 La D´ efense C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 1 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Outline Motivation 1 Material images 2 Challenges and oportunities 3 The special session 4 Conclusions 5 C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 2 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Outline Motivation 1 Material images 2 Challenges and oportunities 3 The special session 4 Conclusions 5 C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 3 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation Periods in mankind’s history are often named after specific materials : stone age bronze age iron age C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 4 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation Periods in mankind’s history are often named after specific materials : Industrial breakthroughs remain related to particular stone age material steel bronze age silicon iron age C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 4 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation Today’s applications Semi-conductors Sensors, Drug carriers, Catalysts, etc. Materials technology is evolving from materials discovered in Nature by chance to designed materials, that repair themselves, adapt to their environment, capture and store energy or information, help elaborate new devices and sensors, etc. Materials are now designed from scratch with initial blueprints, starting from atoms and molecules. Example : Graphene. C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 5 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation Today’s applications Semi-conductors Sensors, Drug carriers, Catalysts, etc. Materials technology is evolving from materials discovered in Nature by chance to designed materials, that repair themselves, adapt to their environment, capture and store energy or information, help elaborate new devices and sensors, etc. Materials are now designed from scratch with initial blueprints, starting from atoms and molecules. Example : Graphene. C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 5 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation The traditional, human, vision-based interpretation of material images misleading... Scanning electron microscopy : Polymer-charged concrete ( c � F. Moreau, IFPEN) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 6 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Motivation The traditional, human, vision-based interpretation of material images misleading... Scanning electron microscopy : Polymer-charged concrete ( c � F. Moreau, IFPEN) Taking physical properties into account... ... is at the heart of sucessful image analysis in material science C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 6 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Outline Motivation 1 Material images 2 Challenges and oportunities 3 The special session 4 Conclusions 5 C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 7 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Catalysts at a coarse level of observation Catalysts with metallic palladium crust ( c � IFPEN). Optical microscopy C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 8 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Catalysts at a coarse level of observation Goals measure the crust thickness (avoids invasive probe techniques) related with the efficiency of catalysts, to improve the conversion of hydrocarbons into Catalysts with metallic palladium crust ( c � IFPEN). chemical products. Optical microscopy C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 8 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Catalysts Scanning electron microscopy : catalyst section. Atomic structure of a ceria nanoparticle ( c � Rhodia). C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 9 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Catalysts Goals 1 st image : characterization of the area in black (cracks), the round shapes (pores) and Scanning electron microscopy : catalyst section. the white dots (zeolite inclusions) 2 nd image : segmentation into pores, ceria, silica Atomic structure of a ceria nanoparticle ( c � Rhodia). C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 9 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Rubber Composite material with elastomer matrix ( c � EADS). Filled rubber’s microstructures ( c � Michelin) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 10 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Rubber Composite material with elastomer matrix ( c � EADS). Goal Filled rubber’s microstructures deduce physical properties ( c from 3D microstructure � Michelin) simulations C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 10 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Outline Motivation 1 Material images 2 Challenges and oportunities 3 The special session 4 Conclusions 5 C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 11 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Classical material image analysis pipeline Modeling 3D Recons- i.e. stochastic truction Preprocessing Segmentation or Image Filtering, Classification or acquisition Registration Analysis Attributes ( shape, distribution...) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 12 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Segmentation Modeling 3D Recons- i.e. stochastic truction Preprocessing Segmentation or Image Filtering, Classification or acquisition Registration Analysis Attributes ( shape, distribution...) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 13 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Segmentation – blob-shaped objects (a) Optimal threshold ; (b) Watershed ; (c) Graph cuts ; (d) Continuous maximum flows [Marak, PhD thesis, 2012] C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 14 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Segmentation – thin objets Issue and technic Issue : Segmenting elongated objects such as fibers is complicated Technic : Continous Max Flows [Appleton, Talbot, PAMI 2006] C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 15 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Analysis Issue and technic Issue : contours of the objects to segment (nanostructured ceriasilica composite catalysts) not well defined Technic : Morphological approach [Moreaud et al., J. of Microscopy 2008] C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 16 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Modeling Modeling 3D Recons- i.e. stochastic truction Preprocessing Segmentation or Image Filtering, Classification or acquisition Registration Analysis Attributes ( shape, distribution...) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 17 / 27

Motivation Material images Challenges and oportunities The special session Conclusions Modeling Modeling 3D Recons- i.e. stochastic truction Preprocessing Segmentation or Image Filtering, Classification or acquisition Registration Analysis Attributes ( shape, distribution...) C. Couprie Image Processing for Materials Characterization: Challenges and Opportunities 18 / 27