Home/Shape-Based Instance Detection Under Arbitrary Viewpoint

Shape-Based Instance Detection Under Arbitrary Viewpoint

Edward Hsiao and Martial Hebert
Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, January, 2013

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Shape-based instance detection under arbitrary viewpoint is a very challenging problem. Current approaches for handling viewpoint variation can be divided into two main categories: invariant and non-invariant. Invariant approaches explicitly represent the structural relationships of high-level, view-invariant shape primitives. Non-invariant approaches, on the other hand, create a template for each viewpoint of the object, and can operate directly on low-level features. We summarize the main advantages and disadvantages of invariant and non-invariant approaches, and conclude that non-invariant approaches are well-suited for capturing fine-grained details needed for specific object recognition while also being computationally efficient. Finally, we discuss approaches that are needed to address ambiguities introduced by recognizing shape under arbitrary viewpoint.

BibTeX Reference
@incollection{Hsiao-2013-17120,
title = {Shape-Based Instance Detection Under Arbitrary Viewpoint},
author = {Edward Hsiao and Martial Hebert},
booktitle = {Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective},
keyword = {instance detection, object detection, arbitrary viewpoint, invariance, view-invariance, view-based, ambiguities},
publisher = {Springer},
month = {January},
year = {2013},
}
2017-09-13T10:39:32+00:00