[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120548-en":3,"doc-seo-120548-105":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},120548,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Connected AI - Merging Mathematical Morphology and Machine Learning","Connected AI presents efficient connected morphological filters that enable filtering, segmentation, and classification of very large images by leveraging mathematical models of perceptual grouping grounded in connectivity. The approach uses connected-component tree representations to extract features that can be scale, rotation, and translation invariant, reducing reliance on data augmentation. It also supports parallel and distributed algorithms, enabling applications across remote sensing, disaster relief, astronomy, microscopy, and medical imaging, with early results in pattern spectra 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: 02-08-2026  \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","cbCaihhRnfKYqyGc","https://ap.wps.com/l/cbCaihhRnfKYqyGc","pdf",6064154,1,2,"English","en",105,"# Introduction\n## Connected Filters: the Principle\n# Pattern Spectra\n## Spunta leaf – R & G channels\n## Self-organising pattern spectra\n# Segmentation in Remote Sensing\n## Differential Attribute Profiles (DAPs)\n# Faint Object Detection in Astronomy\n# Acknowledgments","[{\"question\":\"What are connected morphological filters and what tasks do they support?\",\"answer\":\"They operate on connected components of images and support efficient filtering, segmentation, and classification. Their representation enables extracting features for machine learning and also performing direct image processing tasks.\"},{\"question\":\"How do the extracted features improve machine-learning performance?\",\"answer\":\"Using connected filters as adaptive feature extractors gives machine-learning methods access to non-local information. The features can be invariant to scale, rotation, and translation, lowering the need for common data augmentation.\"},{\"question\":\"Where can this approach be applied according to the poster?\",\"answer\":\"The poster highlights remote sensing for precision agriculture and disaster relief, plus astronomy, microscopy, and medical imaging. It also mentions disease detection in potatoes and tumour segmentation via pattern spectra.\"}]","Connected AI - Merging Mathematical Morphology and Machine Learning | PDF",1785730600,5,{"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/technology/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/connected-ai-merging-mathematical-morphology-and-machine-learning/120548/",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 what tasks do they support?","Question",{"text":74,"@type":75},"They operate on connected components of images and support efficient filtering, segmentation, and classification. Their representation enables extracting features for machine learning and also performing direct image processing tasks.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do the extracted features improve machine-learning performance?",{"text":79,"@type":75},"Using connected filters as adaptive feature extractors gives machine-learning methods access to non-local information. The features can be invariant to scale, rotation, and translation, lowering the need for common data augmentation.",{"name":81,"@type":72,"acceptedAnswer":82},"Where can this approach be applied according to the poster?",{"text":83,"@type":75},"The poster highlights remote sensing for precision agriculture and disaster relief, plus astronomy, microscopy, and medical imaging. 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