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Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots

Chiatti, Agnese; Bardaro, Gianluca; Bastianelli, Emanuele; Tiddi, Ilaria; Mitra, Prasenjit and Motta, Enrico (2019). Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots. In: Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference.

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This position paper presents an attempt to improve the scalability of existing object recognition methods, which largely rely on supervision and imply a huge availability of manually-labelled data points. Moreover, in the context of mobile robotics, data sets and experimental settings are often handcrafted based on the specific task the object recognition is aimed at, e.g. object grasping. In this work, we argue instead that publicly available open data such as ShapeNet can be used for object classification first, and then to link objects to their related concepts, leading to task-agnostic knowledge acquisition practices. To this aim, we evaluated five pipelines for object recognition, where target classes were all entities collected from ShapeNet and matching was based on: (i) shape-only features, (ii) RGB histogram comparison, (iii) a combination of shape and colour matching, (iv) image feature descriptors, and (v) inexact, normalised cross-correlation, resembling the Deep, Siamese-like NN architecture of Submariam et al. (2016). We discussed the relative impact of shape-derived and colour-derived features, as well as suitability of all tested solutions for future application to real-life use cases.

Item Type: Conference or Workshop Item
Copyright Holders: 2019 The Authors
Extra Information: originally presented at the EDBT/ICDT 2019 Joint Conference, Lisbon, Portugal, 26-29 Mar 2019.
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 59508
Depositing User: Agnese Chiatti
Date Deposited: 13 Mar 2019 16:37
Last Modified: 27 Jan 2020 08:06
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