Generic Image Processing - PowerPoint Presentation

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

OutlineIntroductionUsing Climb Developping ClimbGoing FurtherConclusion 2

Introduction 3

Generic Image Processing 4 Images: Roland Levillain. Software Architecture for Generic Image Processing Tools I A H B D C E F G I A H B D C E F G segmentation

Genericity Purpose 5 Graphic : Laurent Najman . Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images algorithms values type structures type segmentation graph 2dmatrix 3dmatrix bool grayscale rgb S x V x A combinations

Climb Generic Image Processing library Common LispInspired by Olena: C++, 10 years old Dynamic vs StaticUsability, maintainability, performance,… Still in beta 6 Compilers sbcl Dependencies iterate , lisp- magick , cl- heap , lisp-unit, cl-gtk2

Architecture7 The different layers of the library Generic types Morphers Image Algorithms Chaining Operator GUI

Using Climb 8

Algorithms 9 original grayscale median filter Otsu threshold dilation erosion jitter

( 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 10 (save (erosion (otsu (image-load ‘images/lena128gray.jpg’)) (4-connectivity)) ‘out/lena-eroded.png’) image-load ‘images/lena128gray.jpg’otsu dilation (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 otsu to-gray image- load Chaining Operator 11 More complex chaining

DevelopPing Climb 12

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 13 I A H B D C E F G Image access image( site ) = value ( setf ( iref image site) value) Generalization : Lisp:

( value-inverse ) (iref image site) ( setf ( iref image site) ) Values and Sites 14 Values: Site: bool grayscale RGB x y I A H B D C E F G 2d-point label

Browsing Images 15 x y Site-set: ( let ( ) ( loop :for s := : while s )) ( domain ( image- domain image)) ( site-set- next domain ) … I A H B D C E F G

( let ( ) (loop :for s := : while s )) … ( site-set- next neighbors ) ( neighbors ( site-set- window window site)) Browsing Images 16 x y Site-set- window : I A H B D C E F G

Morphers 17 Transforming images with morphers Value morpher Structure morpher

Going further 18

Properties 19 Adapting genericity algorithms values type structures type segmentation graph 2dmatrix 3dmatrix bool grayscale rgb support:regular

Value: GUI 20 Climb based interface I A H B D C E F G GTK-BOX Morpher Resize climb graph

Conclusion 21 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 GenericityUsability Performance

Questions ?Thanks for your attention, 22

SourcesTh. 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 ToolsLaurent Najman . Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images 23