Perspective Transformation Layer

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on the affine transformation that is insufficient to reflect such geometric position changes. Furthermore, current solutions often apply a neural network module to learn a single transformation matrix, which not only ignores the importance of multi-view analysis but also includes extra training parameters from the module apart from the transformation matrix parameters that increase the model complexity. In this paper, a perspective transformation layer is proposed in the context of deep learning. The proposed layer can learn homography, therefore reflecting the geometric positions between observers and objects. In addition, by directly training its transformation matrices, a single proposed layer can learn an adjustable number of multiple viewpoints without considering module parameters. The experiments and evaluations confirm the superiority of the proposed layer.

Identifier

85171996523 (Scopus)

ISBN

[9798350320282]

Publication Title

Proceedings 2022 International Conference on Computational Science and Computational Intelligence Csci 2022

External Full Text Location

https://doi.org/10.1109/CSCI58124.2022.00250

First Page

1395

Last Page

1401

Grant

CIF/SaTC-2104267

Fund Ref

National Science Foundation

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