[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126482-en":3,"doc-seo-126482-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126482,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Hexagonal Image Processing in the Context of Machine Learning - Conception of a Biologically Inspired Hexagonal Deep Learning Framework","Hexagonal image processing in machine learning is developed by drawing inspiration from human visual perception. The work proposes image processing systems that combine biologically motivated evolutionary structures, aiming to leverage advantages over conventional square-lattice approaches used by many recording and output devices. It introduces Hexnet, the hexagonal transformation pipeline, and dependent methods. Experiments on a test environment show improved hexagonal image processing performance, including benefits for hexagonal artificial neural networks (H-DNN), with reduced trainable parameters and higher training and test rates.","Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework  \nTobias Schlosser, Michael Friedrich, and Danny Kowerko  \nJunior Professorship of Media Computing,  \nChemnitz University of Technology,  \n09107 Chemnitz, Germany,  \n{ [firstname.lastname}@cs.tu-chemnitz.de](firstname.lastname}@cs.tu-chemnitz.de)  \narXiv : 19 11 . 11251v3 [ cs .LG] 26 Jan 2020  \nAbstract—Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state of the art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach with the synthesis of the therefor designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterpart by transforming classical square lattice based datasets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.  \nIndex Terms—Computer Vision, Pattern and Image Recognition, Deep Learning, Convolutional Neural Networks, Hexagonal Image Processing, Hexagonal Lattice, Hexagonal Sampling  \nI. INTRODUCTION AND MOTIVATION  \nFollowing the developments of recent years, machine learning methods in the form of artificial neural networks are becoming increasingly important. Convolutional neural networks (CNN) for object recognition and classification in this context are one of the approaches that are in the focus of current research in the domain of deep learning. In order to meet the ever-increasing demands of more complex problems, novel application areas, and larger data sets, following Krizhevsky et al. [1], more and more novel models and procedures are being developed, as their complexity and diversity following Szegedyet al. [2] steadily increases. However, novel architectures for artificial neural networks, including convolutional and pooling layers, are being developed that exceed the conventional cartesian-based architectures. These include, but are not limited to, spherical or non-Euclidean manifolds, as current research by Bronstein et al. [3] demonstrates.  \nWhile the structure and functionality of artificial neural networks is inspired by biological processes, they are also limited by their underlying structure due to the current state of the art of recording and output devices. Therefore, mostly square structures are used, which also significantly restrict subsequent image processing systems, as the set of allowed operations is reduced depending on the arrangement of the underlying structure [4] .  \nIn comparison to the current state of the art in machine learning, the human visual perception system suggests an alternative, evolutionarily-based structure, which manifests itself in the human eye. The retina of the human eye displays according to Curcio et al. [5] a particularly strong hexagonal arrangement of sensory cells, whereas following Middleton and Sivaswamy [6] the processing of incoming signals is handled much more efficiently. Through the layers of the retina the reduction of the received information to the nerve fibers takes place, which are connected to the brain via afferents over the optic nerve.","cbCaid1dHBDerDBD","https://ap.wps.com/l/cbCaid1dHBDerDBD","pdf",4008904,3,1,"English","en",105,"# Introduction and Motivation\n## Related Work","[{\"question\":\"What problem does hexagonal image processing address compared with square-based methods?\",\"answer\":\"Square-based image processing systems restrict subsequent operations due to the underlying cartesian structure. Hexagonal lattice formats offer a more suitable alternative inspired by biological vision and can improve transformation efficiency and related processing outcomes.\"},{\"question\":\"What is Hexnet in the proposed framework?\",\"answer\":\"Hexnet is the designed hexagonal image processing framework that synthesizes the processing steps of hexagonal image transformation and related dependent methods for machine learning contexts.\"},{\"question\":\"How do hexagonal neural networks (H-DNN) relate to conventional square-lattice datasets?\",\"answer\":\"Classical square-lattice datasets can be transformed into hexagonal representations, enabling comparison between hexagonal and square architectures while potentially reducing trainable parameters and improving training and test rates.\"}]","Hexagonal Image Processing in the Context of Machine Learning - Conception of a Biologically Inspired Hexagonal Deep Learning Framework | PDF",1785905306,20,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hexagonal-image-processing-in-the-context-of-machine-learning-conception-of-a-biologically-inspired-hexagonal-deep-learning-framework","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/hexagonal-image-processing-in-the-context-of-machine-learning-conception-of-a-biologically-inspired-hexagonal-deep-learning-framework/126482/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-17","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does hexagonal image processing address compared with square-based methods?","Question",{"text":75,"@type":76},"Square-based image processing systems restrict subsequent operations due to the underlying cartesian structure. Hexagonal lattice formats offer a more suitable alternative inspired by biological vision and can improve transformation efficiency and related processing outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is Hexnet in the proposed framework?",{"text":80,"@type":76},"Hexnet is the designed hexagonal image processing framework that synthesizes the processing steps of hexagonal image transformation and related dependent methods for machine learning contexts.",{"name":82,"@type":73,"acceptedAnswer":83},"How do hexagonal neural networks (H-DNN) relate to conventional square-lattice datasets?",{"text":84,"@type":76},"Classical square-lattice datasets can be transformed into hexagonal representations, enabling comparison between hexagonal and square architectures while potentially reducing trainable parameters and improving training and test rates.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]