[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120554-en":3,"doc-seo-120554-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120554,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Connected AI - Merging Mathematical Morphology and Machine Learning","Connected AI presents connected morphological filters as adaptive feature extractors for machine-learning, enabling efficient filtering, segmentation, and classification of very large images using models of perceptual grouping via connectivity. The approach builds tree representations of connected components across threshold levels, achieving fast computation and providing rotation- and scale-invariant features without relying on common data augmentation. Applications span remote sensing for precision agriculture and disaster relief, astronomy for faint object detection, microscopy for pattern spectra-based classification, and medical imaging concepts such as tumor segmentation and disease detection.","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: 01-01-2026  \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","cbCaio6O8PK0TElt","https://ap.wps.com/l/cbCaio6O8PK0TElt","pdf",6063839,1,2,"English","en",105,"# Introduction\n## Connected Filters: the Principle\n## Pattern Spectra\n## Segmentation in Remote Sensing\n## Faint Object Detection in Astronomy\n## Acknowledgments","[{\"question\":\"What are connected morphological filters and why are they useful in machine learning?\",\"answer\":\"They operate on connected components rather than arbitrary pixel neighborhoods and can efficiently support filtering, segmentation, and classification. Used as adaptive feature extractors, they provide machine-learning methods access to non-local information based on connectivity.\"},{\"question\":\"How do connected filters achieve invariance properties?\",\"answer\":\"They can include rotation and scale invariance, not only translation invariance. This reduces the need for typical data augmentation strategies.\"},{\"question\":\"What applications are discussed for the proposed connected AI approach?\",\"answer\":\"The poster highlights remote sensing for precision agriculture and earthquake relief, astronomy for faint object detection, microscopy for diatom classification, and medical imaging-related directions like tumor segmentation and disease detection in potatoes.\"}]","Connected AI - Merging Mathematical Morphology and Machine Learning | PDF",1785730625,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"connected-ai-merging-mathematical-morphology-and-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/connected-ai-merging-mathematical-morphology-and-machine-learning/120554/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What are connected morphological filters and why are they useful in machine learning?","Question",{"text":74,"@type":75},"They operate on connected components rather than arbitrary pixel neighborhoods and can efficiently support filtering, segmentation, and classification. Used as adaptive feature extractors, they provide machine-learning methods access to non-local information based on connectivity.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do connected filters achieve invariance properties?",{"text":79,"@type":75},"They can include rotation and scale invariance, not only translation invariance. This reduces the need for typical data augmentation strategies.",{"name":81,"@type":72,"acceptedAnswer":82},"What applications are discussed for the proposed connected AI approach?",{"text":83,"@type":75},"The poster highlights remote sensing for precision agriculture and earthquake relief, astronomy for faint object detection, microscopy for diatom classification, and medical imaging-related directions like tumor segmentation and disease detection in potatoes.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]