{"id":8,"date":"2022-12-07T12:30:00","date_gmt":"2022-12-07T12:30:00","guid":{"rendered":"https:\/\/tetra.ucll.be\/?page_id=8"},"modified":"2023-01-25T09:24:49","modified_gmt":"2023-01-25T09:24:49","slug":"8-2","status":"publish","type":"page","link":"https:\/\/tetra.ucll.be\/","title":{"rendered":"Home"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”About” module_id=”classificatieai” _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.16″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.19.2″ _module_preset=”b6cb9bd8-0fbd-4699-b58d-51b3d480db23″ header_2_font=”Candal|||on|||||” header_2_text_color=”#000000″ header_2_font_size=”60px” header_2_line_height=”1.1em” header_2_font_size_tablet=”30px” header_2_font_size_phone=”22px” header_2_font_size_last_edited=”on|phone” header_2_line_height_tablet=”1.2em” header_2_line_height_phone=”1.3em” header_2_line_height_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

Classificatie point clouds met AI<\/span><\/h2>\n

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TETRA AI Proposal 1<\/span><\/h6>\n

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TOPIC<\/span><\/h4>\n

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Door gebruik te maken van een LiDAR-camera kan er op een eenvoudige manier een point cloud gegenereerd worden dat 1 op 1 een mapping kan maken van een omgeving of object. Met behulp van verschillende softwarepakketten kunnen specifieke bewerkingen uitgevoerd worden op deze point clouds, zoals het berekenen van de afstand tussen punten. Echter is hiervoor specifieke kennis vereist en kost het ook enorm veel tijd om bewerkingen steeds manueel uit te voeren. Dit zorgt ervoor dat point clouds te weinig gebruikt worden om berekeningen op uit te voeren en er nog steeds beroep gedaan wordt op conventionele meetmethoden, terwijl LiDAR-technologie al zeer nauwkeurige point clouds kan genereren. Door gebruik te maken van AI voor het classificeren van types oppervlaktes in een point cloud, kunnen er ook zeer snel automatisch berekeningen gemaakt worden met deze oppervlaktes.<\/span><\/p>\n

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Projectdoel<\/span><\/h4>\n

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De beoogde uitkomst is een framework dat op basis van trainingsdata automatisch oppervlaktes\/objecten kan classificeren en segmenteren en hiervan, bijvoorbeeld, de oppervlaktes kan berekenen en het aantal ervan weergeven. De bedoeling is dat we op termijn aan de hand van unsupervised learning een zelftrainend AI-model hebben, dat datasets automatisch volledig classificeert, mogelijks nog wel met behulp van\u00a0 een omschrijving van de point cloud. Om classificaties en segmentaties te controleren en verbeteren, zal er ook een simplistische GUI aangemaakt worden. De data die gecontroleerd werd kan dan ook automatisch toegevoegd worden aan de trainingsdata om het AI-algoritme te laten bijleren en accurater te laten worden.<\/span><\/p>\n

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Impact<\/span><\/h4>\n

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Uiteindelijke zal de uitkomst van dit project voor zeer diverse sectoren bruikbaar zijn. Zo is er bijvoorbeeld zeer veel vraag in de bouwindustrie om, bijvoorbeeld, opmetingen te maken van gebouwen of opmetingen te maken bij grote constructies of wegenwerken. Maar zijn er ook toepassingen in de agrocultuur of de gezondheidssector, waar ook al gebruik gemaakt wordt van point clouds.<\/span><\/p>\n

Heb je interesse om op de hoogte gehouden te blijven rond dit onderzoek en\/of deel te nemen aan de gebruikersgroep? Contacteer ons dan via onderstaand formulier of via david.vandenbroeck@ucll.be.<\/span><\/p>\n

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Meer informatie?<\/span><\/h1>\n

Voor meer informatie over dit project, of voor een een zeteltje in de stuurgroep van dit project, neem contact op met:<\/strong><\/span><\/p>\n

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David.vandenbroeck@ucll.be<\/span><\/a><\/h5>\n

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<\/h1>\n

Voor meer informatie over Tetra-projecten in het algemeen, bekijk:<\/strong><\/span><\/p>\n

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TETRA-projecten VLAIO<\/a><\/h5>\n

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Heb je interesse in dit topic? Laat het ons via onderstaand formulier weten en we nemen zo snel mogelijk contact met jou op!<\/span><\/strong><\/h3>\n

[\/et_pb_text][et_pb_contact_form captcha=”off” email=”david.vandenbroeck@ucll.be” custom_message=”Email received through tetra.ucll.be Classificatie pointclouds met AI||et_pb_line_break_holder||||et_pb_line_break_holder||CONTACTFORMULIER INTERESSE||et_pb_line_break_holder||||et_pb_line_break_holder||Naam: %%Naam%%||et_pb_line_break_holder||Email: %%Email%% ||et_pb_line_break_holder||Bedrijf + functie: %%Bedrijf%%||et_pb_line_break_holder||Bericht: %%Bericht%%||et_pb_line_break_holder||” success_message=”Bedankt voor je interesse! 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Smart TV-Aid<\/span><\/h2>\n

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Tetra AI proposal 2<\/span><\/h6>\n

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TOPIC<\/span><\/h4>\n

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Quasi elk woonzorgcentrum, ziekenhuiskamer, revalidatiecentrum, … is tegenwoordig uitgerust met een Smart TV die onderhuids (een variant van) Android draaien en daardoor eigenlijk zeer geschikt zijn om naast gewone tv-uitzendingen, ook HTML-pagina\u2019s en applicaties te tonen (zoals het octopUZ systeem van het UZ Leuven wat samen met Televic & Lincor is uitgewerkt).<\/span><\/p>\n

Omdat deze TV\u2019s dus beschouwd kunnen worden als grote tablets (niet altijd met een touchscreen, maar dat hoeft ook niet), zouden we deze ook kunnen gebruiken om gepersonaliseerde info te kunnen aanbieden. In stijgende lijn van complexiteit denken we hierbij, bijvoorbeeld, aan:<\/span><\/p>\n