Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification

Sameera Ramasinghe; Salman Khan; Salman Khan; Nick Barnes

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification

Topics: Deep Learning and Neural Networks; Machine Learning Methods; Shape Representation

In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 115-125, 2020 , Valletta, Malta

Authors: Sameera Ramasinghe ¹ ; Salman Khan ^{1

;

2} and Nick Barnes ¹

Affiliations: ¹ Australian National University, Australia ; ² Inception Institute of Artificial Intelligence, U.A.E.

Keyword(s): Convolution Neural Networks, Volumetric Convolution, Zernike Polynomials, Deep Learning.

Abstract: Convolution is an effective technique that can be used to obtain abstract feature representations using hierarchical layers in deep networks. However, performing convolution in non-Euclidean topological spaces such as the unit ball (B3) is still an under-explored problem. In this paper, we propose a light-weight experimental architecture for 3D object classification, that operates in B3. The proposed network utilizes both hand-crafted and learned features, and uses capsules in the penultimate layer to disentangle 3D shape features through pose and view equivariance. It simultaneously maintains an intrinsic co-ordinate frame, where mutual relationships between object parts are preserved. Furthermore, we show that the optimal view angles for extracting patterns from 3D objects depend on its shape and achieve compelling results with a relatively shallow network, compared to the state-of-the-art.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.145.163.58

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Ramasinghe, S.; Khan, S. and Barnes, N. (2020). Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-397-1; ISSN 2184-4313, SciTePress, pages 115-125. DOI: 10.5220/0009344801150125

@conference{icpram20,
author={Sameera Ramasinghe. and Salman Khan. and Nick Barnes.},
title={Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2020},
pages={115-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009344801150125},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification
SN - 978-989-758-397-1
IS - 2184-4313
AU - Ramasinghe, S.
AU - Khan, S.
AU - Barnes, N.
PY - 2020
SP - 115
EP - 125
DO - 10.5220/0009344801150125
PB - SciTePress