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Learning from Depth Sensor Data using Inductive Logic Programming


Drole, Miha; Vračar, Petar; Stančić, Ivo; Musić, Josip; Panjkota, Ante; Kononenko, Igor; Kukar, Matjaž
Learning from Depth Sensor Data using Inductive Logic Programming // Proceedings of the 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT) / Ribić, Samir ; Zajko, Ernedin ; Sadžak, Aida (ur.).
Sarajevo: IEEE, 2015. (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Learning from Depth Sensor Data using Inductive Logic Programming

Autori
Drole, Miha ; Vračar, Petar ; Stančić, Ivo ; Musić, Josip ; Panjkota, Ante ; Kononenko, Igor ; Kukar, Matjaž

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT) / Ribić, Samir ; Zajko, Ernedin ; Sadžak, Aida - Sarajevo : IEEE, 2015

ISBN
978-1-4673-8145-1

Skup
2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT)

Mjesto i datum
Sarajevo, Bosna i hercegovina, 29-31.10.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Supervised learning; context awareness; assistive devices; knowlegde discovery

Sažetak
The problem of detecting objects and their movements in sensor data is of crucial importance in providing safe navigation through both indoor and outdoor environments for the visually impaired. In our setting we use depth- sensor data obtained from a simulator and use inductive logic programming (ILP), a subfield of machine learning that deals with learning concept descriptions, to learn how to detect borders, find the border that is nearest to some point of interest, and border correspondence through time. We demonstrate how ILP can be used to tackle this problem in an incremental manner by using previously learned predicates to construct more complex ones. The learned concept descriptions show high (> 90%) accuracy and their natural language interpretation closely matches an intuitive understanding of their meaning.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, strojarstva i brodogradnje, Split