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By operating on connected components via hierarchical tree representations, the approach enables efficient filtering, segmentation, and classification of large images while providing rotation and scale invariance. The poster highlights applications including pattern spectra for explainable classification, remote sensing segmentation with Differential Attribute Profiles, faint object detection in astronomy, and medical or microscopy use cases.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/connected-ai-merging-mathematical-morphology-and-machine-learning-poster-session/290817/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/connected-ai-merging-mathematical-morphology-and-machine-learning-poster-session/290817.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem do connected morphological filters address for image analysis?","Question",{"text":112,"@type":113},"They provide efficient filtering, segmentation, and classification by using mathematical models of perceptual grouping based on connectivity rather than arbitrary pixel neighborhoods.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do connected filters support invariance compared with common image-processing workflows?",{"text":117,"@type":113},"Their extracted features can be rotation- and scale-invariant (in addition to translation invariance), which reduces reliance on typical data augmentation methods.",{"name":119,"@type":110,"acceptedAnswer":120},"What are pattern spectra and how are they used in practice?",{"text":121,"@type":113},"Pattern spectra compute a 2D histogram over shape and size features of connected components across threshold levels, supporting tasks like microscopy classification and prototype-based explainable disease detection.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290817,1789642623,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","University of Groningen  \nConnected AI  \nWilkinson, M. H. F. ; Bunte, Kerstin  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nFinal author's version (accepted by publisher, after peer review)  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nWilkinson, M. H. F. , & Bunte, K. (2025) . Connected AI: Merging Mathematical Morphology and Machine Learning. Poster session presented at AI-Grunn, Groningen, Netherlands.  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 30-12-2025  \nConnected AI: Merging Mathematical Morphology and Machine Learning  \nMichael H. F . Wilkinson, Kerstin Bunte,  \nIntelligent Systems Group, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence,  \nUniversity of Groningen, Groningen, the Netherlands  \n[m. h. f. wilkinson@rug. nl](m. h. f. wilkinson@rug. nl) , [k. bunte@rug. nl](k. bunte@rug. nl)  \n\n| \u003Cbr>Introduction\u003Cbr>Connected morphological filters allow efficient filtering, segmentation and classification of images of hude sizes, using mathematical models of perceptual grouping based on connectivity. Using them as adaptive feature extractors for machine-learning methods gives the latter access to non-local information, in ways not possible with other techniques. Furthermore, the features extracted can be scale, rotation, and translation invariant, removing the need for common data augmentation methods. These abilities, plus the development of efficient parallel and distributed algorithms allows application in fields such as remote sensing for precision agriculture using drones, and disaster relief using satellite or aerial images, but also in astronomy, microscopy and medical imaging. This poster gives an overview of the advances so far. |  | \u003Cbr>Connected Filters: the Principle\u003Cbr>Connected filters operate at the level of connected components of images, rather than arbitrary surroundings of pixels. Images are typically transformed into tree representations containing the nesting relations of connected components of different threshold levels in grey scale, and different degrees of colour dissimilarity in colour and hyperspectral cases . These trees can be used for feature extraction but also for image filtering, segmentation and classification. Building the tree typically has O (Nlog N) time complexity, and O (N) memory complexity. Filtering and segmentation are far faster, and typically O (N) . A unique feature of these filters is their ability to include rotation and scale invariance, rather than only translation invariance. |\n| --- | --- | --- |\n|  |  |  |\n| \u003Cbr>Pattern Spectra\u003Cbr>\u003Cbr","cbCaicVUE6wFq7aD","https://ap.wps.com/l/cbCaicVUE6wFq7aD","pdf",6063814,"English","# Introduction\n# Connected Filters: the Principle\n# Pattern Spectra\n# Segmentation in Remote Sensing\n# Faint Object Detection in Astronomy\n# Acknowledgments","[{\"question\":\"What problem do connected morphological filters address for image analysis?\",\"answer\":\"They provide efficient filtering, segmentation, and classification by using mathematical models of perceptual grouping based on connectivity rather than arbitrary pixel neighborhoods.\"},{\"question\":\"How do connected filters support invariance compared with common image-processing workflows?\",\"answer\":\"Their extracted features can be rotation- and scale-invariant (in addition to translation invariance), which reduces reliance on typical data augmentation methods.\"},{\"question\":\"What are pattern spectra and how are they used in practice?\",\"answer\":\"Pattern spectra compute a 2D histogram over shape and size features of connected components across threshold levels, supporting tasks like microscopy classification and prototype-based explainable disease detection.\"}]","Connected AI - Merging Mathematical Morphology and Machine Learning - Poster session | PDF"]