Generic Image Processing with Climb Laurent Senta – Didier Verna LRDE EPITA Research Lab April 30, 2012 lrde.epita.fr.

Generic Image Processing with Climb

Laurent Senta – Didier Verna

LRDEEPITA Research Lab

April 30, 2012

lrde.epita.fr

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Outline

• Introduction• Using Climb• Developping Climb• Going Further• Conclusion

INTRODUCTION

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Generic Image Processing

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Images: Roland Levillain. Software Architecture for Generic Image Processing Tools

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segmentation

Genericity Purpose

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Graphic: Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images

algorithms

valuestype

structurestype

segmentation

graph

2dmatrix

3dmatrix

bool grayscale rgb

S x V x A combinations

Climb

• Generic Image Processing library– Common Lisp

• Inspired by Olena: C++, 10 years old– Dynamic vs Static– Usability, maintainability, performance,…

• Still in beta

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Compilers sbcl

Dependencies iterate, lisp-magick, cl-heap, lisp-unit, cl-gtk2

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ArchitectureThe different layers of the library

Generic types Morphers

Image Algorithms Chaining Operator

GUI

USING CLIMB

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Algorithms

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original grayscale

median filter

Otsu threshold

dilation erosionjitter

(setf img (image-load ‘images/lena128gray.jpg’))(setf ot-img (otsu img))(setf dil-img (dilation ot-img (4-connectivity)))(save dil-img ‘out/lena-dilated.png’)

Chaining OperatorChaining algorithms with the $ operator

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(save (erosion (otsu (image-load ‘images/lena128gray.jpg’)) (4-connectivity)) ‘out/lena-eroded.png’)

image-load ‘images/lena128gray.jpg’otsudilation (4-connectivity)save ‘out/lena-eroded.png’

($ (image-load ‘images/lena128gray.jpg’) (otsu) (dilation (4-connectivity)) (save ‘out/lena-eroded.png’))

chain

dilation

diff

erosion

otsuto-grayimage-load

Chaining Operator

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More complex chaining

DEVELOPPING CLIMB

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• matrix[x, y] → pixelvalue

• graph.getNode(label) → nodevalue

• model[x, y, z] → voxelvalue

• matrix[x, y] → pixelvalue

• graph.getNode(label) → nodevalue

• model[x, y, z] → voxelvalue

Image Definition

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Image access

image(site) = value

(setf (iref image site) value)

Generalization:

Lisp:

(value-inverse ) (iref image site)(setf (iref image site) )

Values and Sites

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Values:

Site:

bool grayscale RGB

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2d-point label

Browsing Images

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x

y

Site-set:

(let ( ) (loop :for s := :while s ))

(domain (image-domain image))(site-set-next domain)

…

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(let ( ) (loop :for s := :while s )) …

(site-set-next neighbors) (neighbors (site-set-window window site))

Browsing Images

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x

y

Site-set-window:

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MorphersTransforming images with morphers

Value morpher Structure morpher

GOING FURTHER

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PropertiesAdapting genericity

algorithms

valuestype

structurestype

segmentation

graph

2dmatrix

3dmatrix

bool grayscale rgb

support:regular

Value:

GUI

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Climb based interface

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GTK-BOX

Morpher Resize

climb graph

Conclusion

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Current status

Image Processing Practitionner Algorithm implementor

• Built-in algorithms• Composition tools• Chaining operator• Morphers

• GUI

• High-level domain model• Generic values• Generic structures• Generic implementations

Hot topics

Genericity Usability Performance

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QUESTIONS ?Thanks for your attention,

Sources

• Th. Géraud and R. Levillain. Semantics-driven genericity: A sequel to the static C++ object-oriented programming paradigm (SCOOP 2).

• R. Levillain, Th. Géraud, and L. Najman. Why and how to design a generic and efficient image processing framework: The case of the Milena library.

• N. Otsu. A threshold selection method from gray-level histograms.• P. Soille. Morphological Image Analysis: Principles and Applications• Roland Levillain. Software Architecture for Generic Image Processing Tools• Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images

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