| 59508 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 59508 |
Creator |
81226b931d8904b22774bbfe1edd33a0 |
| 59508 |
Creator |
b934d41b07200c47dc3d135d05960f70 |
| 59508 |
Creator |
e384860a4074d04e7761c8bd9ee3f050 |
| 59508 |
Creator |
cacb9bf9d6f500b32ecbd3751165bc53 |
| 59508 |
Creator |
4abdf0939f1bcdf3f92bb573766237ea |
| 59508 |
Creator |
ext-874ab61316d7b2c5bd092e0ec5f9c567 |
| 59508 |
Date |
2019 |
| 59508 |
Is Part Of |
repository |
| 59508 |
abstract |
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. |
| 59508 |
authorList |
authors |
| 59508 |
presentedAt |
ext-e07ffd778b67decf6ee039013d69cc7c |
| 59508 |
status |
peerReviewed |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800635 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800636 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800641 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800642 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800643 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/800644 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/806127 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899005 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899006 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899007 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899008 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899009 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899010 |
| 59508 |
uri |
http://data.open.ac.uk/oro/document/899011 |
| 59508 |
type |
AcademicArticle |
| 59508 |
type |
Article |
| 59508 |
label |
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. |
| 59508 |
label |
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. |
| 59508 |
Title |
Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots |
| 59508 |
in dataset |
oro |