Generic Image Processing With Climb - Slides
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This document summarizes the Climb image processing library. Climb allows generic image processing through algorithms that can process different image types defined by their values (e.g. bool, grayscale, RGB) and sites (e.g. 2D points, graphs). The library provides common algorithms, chaining operators to combine them, and a GUI. It aims to balance genericity with usability and performance. The document outlines Climb's architecture, use, development aspects like the image definition, and potential future directions around genericity, usability, and performance.
![Image Definition
I H B
G C
A
D
F
E
Image access
• matrix[x, y] → pixelvalue
• graph.getNode(label) → nodevalue
• model[x, y, z] → voxelvalue
Generalization: image(site) = value
Lisp: (setf (iref image site) value)
13](https://image.slidesharecdn.com/climb-v6-120502060810-phpapp02/85/Generic-Image-Processing-With-Climb-Slides-13-320.jpg)
Generic Image Processing With Climb - Slides
- 1. Generic Image Processing with Climb Laurent Senta – Didier Verna April 30, 2012 LRDE EPITA Research Lab lrde.epita.fr
- 2. Outline • Introduction • Using Climb • Developping Climb • Going Further • Conclusion 2
- 4. Generic Image Processing Images: Roland Levillain. Software Architecture for Generic Image Processing Tools I H B G C A D F E segmentation I H B G C A D F E 4
- 5. Genericity Purpose Graphic: Laurent Najman. Point de vue d'un théoricien sur l'intérêt de la généricité pour le traitement d'images algorithms segmentation S x V x A combinations values type graph bool grayscale rgb 2dmatrix 3dmatrix structures 5 type
- 6. Climb • Generic Image Processing library – Common Lisp • Inspired by Olena: C++, 10 years old – Dynamic vs Static – Usability, maintainability, performance,… • Still in beta Compilers sbcl Dependencies iterate, lisp-magick, cl-heap, lisp-unit, cl-gtk2 6
- 7. Architecture The different layers of the library GUI Chaining Image Algorithms Operator Generic types Morphers 7
- 9. Algorithms original grayscale Otsu threshold jitter median filter dilation erosion 9
- 10. Chaining Operator Chaining algorithms with the $ operator (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’) (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’)) 10
- 11. Chaining Operator More complex chaining erosion image-load to-gray otsu diff dilation 11
- 13. Image Definition I H B G C A D F E Image access • matrix[x, y] → pixelvalue • graph.getNode(label) → nodevalue • model[x, y, z] → voxelvalue Generalization: image(site) = value Lisp: (setf (iref image site) value) 13
- 14. Values and Sites Values: bool grayscale RGB Site: I H B x G C A D F E y 2d-point label (setf (iref image site) (value-inverse (iref image site))) 14
- 15. Browsing Images Site-set: x I H B G C A D F E y (let ((domain (image-domain image))) (loop :for s := (site-set-next domain) :while s …)) 15
- 16. Browsing Images Site-set-window: x I H B G C A D F E y (let ((neighbors (site-set-window window site))) (loop :for s := (site-set-next neighbors) :while s …)) 16
- 17. Morphers Transforming images with morphers Value morpher Structure morpher 17
- 18. GOING FURTHER 18
- 19. Properties Adapting genericity algorithms support:regular segmentation values type graph bool grayscale rgb 2dmatrix 3dmatrix structures 19 type
- 20. GUI Climb based interface Value: I H B GTK-BOX G C A Morpher D Resize F E climb graph 20
- 21. Conclusion Current status Image Processing Practitionner Algorithm implementor • Built-in algorithms • High-level domain model • Composition tools • Generic values • Chaining operator • Generic structures • Morphers • Generic implementations • GUI Hot topics Genericity Usability Performance 21
- 22. Thanks for your attention, QUESTIONS ? 22
- 23. 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 23
Editor's Notes
- #3: Starts bydescribing Image Processing and whywe are looking for genericityThenwe’lltake a look atClimbfromdifferent point of view: First: as a IP researchersthat uses the toolsweprovide to transformits images Second: as a Developperthatwants to implements new algorithms in the libraryFinally, I’ll show you prototypes that are not fully part of the libraryyetDon’thesitate to ask question any time if I’m not clear
- #4: Let’s talk about Image Processing
- #11: Usingsetf and variable to store the intermediateresultsChaining all the algo call in one bigprocedureHowever, the first method: repetitive, many variable, namemistakes = errorsthatcouldbe hard to find the second methodmay push the algorithmparametersaway, hard to understand and modificateWhatwewant to do as IP researchers:chainmanyalgorithm andthe librarywouldimplicitlyunderstandthateachresultismeant to bepassed to the nextprocessingThanks to the Lisp macro system, Climbprovides the $ operatorthatwill replace the first parameter of each call by the result of the previous one.Usingthis IP-orientedsyntax, wecanwritereadblechain of algorithmwithouthaving to deal withmanyrepetition > Best of both world[Demo: erosion](loader le lena-otsu!)
- #12: Now, sequence of algo: too simpleOftenyouwan to branch the processing in order to construct more complexoperatorsHereis an example of a basic border detectionfilterthatworks by computing the diffbetween the eroded and dilated version of the same image.$-operatordedicated to IP, soanothersyntaxisadded to support thisparadigmwith the 2 slash operator[Demo]I’mgoing to applythisfilter on a a new image,Copy and paste the code fromels.rtWhathappen ?Add the //But I’mmaywant to call functionthat are not part of the image processlikeloggingAdd the ‘(timer-start) ‘(timer-print ‘’Time:’)CallDetailresultsShow the printed messageThis toolis important to us because shows capabilitiesof Lisp to transformitselfconstructDSL not simplydedicated to complex programmer trick but canbeusedto adapt the language to the userswe’reaiming for.
- #13: Well,nowlet’s a look at the Genericity layer and how wecanadd new algorithm in the library
- #16: Site-set, set of sites used to browse an areaThey are iterator that returns a site each time you call site-set-next[Slide1-2-3]retrieve all the site for a given image[Slides]Explain line image-domain, call code on s (could be the previous code) no-info underlying typeDemo: InvertNow that we can browse a while image, we also want to browse a specific area,That we call the neighborhood of a site.
- #17: Anothercommon patternBrowsing a neighborhood allows us to compute local information,And for example, in the segmentation algorithm, find the sitesThat are close, in the value space but also in the structure spaceBrowse a neighborhood: site-set centered on a site and retrieve all it's neighborhoodsUsed for example to compute the mean value in the mean filter or in the erosion/dilationTo spread the values to their neighborsExplain lineAgain, no infoLet’swrite a bluralgorithm[DEMO]
- #18: In order to finish with the climbdeveloppement, let’stake a look atmorphers.Morpher are objectsbuiltaround an image thatwilltransformsome of it’spropertiesdynamically.Values: transforme the valuesFor example: gray morpher, convert values, image saw by the outside world as grayscaleStructure: Transform the structuresFor example: restrictmorpher, the image saw by the outside world as a smaller oneAvoidWaste of memorySimplifygenericitysinceyoucaneasilyconvert an image that do not fit the prerequisite for an algorithmA thresholdalgorithmthatneeds a grayscale imageAn algorithmthatneeds an image with a size being a power of 2Slower ? Not necessarily: morpherblurwithmorpherresize[Demomorpherblur]
- #19: NowI’mgoing to talk about prototype thingswe’reworking on
- #21: these algo are available in a GUI Provide the algorithm without knownledge in programming interesting: based on the generic types defined in ClimbClimb based GUI: climb graph for the algo call and the gtk displaymorpher used for image resizing from the computation to the display (no duplication)Interesting: library generic enough to allows unusual uses of the types[Demo] run the previous $-chain using the GUIAdvantage over $: Cycle for density computation,…
- #22: Conclusion:Even if Climbis not yet a stable generic IP library, we have been able to developtoolsthat shows how Lisp canbe an interestinglanguage in a domainwere C and C++ are king.Westill have somedevbeforebeing able to release it, howeverheresome point of enhancementwe’dlike to be able to work ongenericity and usability: automorphing performances: bench the wholelibraryusability: goingfurtherwith the gui by discoveringautomatically the algorithms and parameters
- #23: Label, applicable ?, rIsize