Shape-Based Instance Detection Under Arbitrary Viewpoint

Edward Hsiao and Martial Hebert
Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, Sven Dickinson and Zygmunt Pizlo, ed., Springer, 2013


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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.

Keywords
instance detection, object detection, arbitrary viewpoint, invariance, view-invariance, view-based, ambiguities

Notes

Text Reference
Edward Hsiao and Martial Hebert, "Shape-Based Instance Detection Under Arbitrary Viewpoint," Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, Sven Dickinson and Zygmunt Pizlo, ed., Springer, 2013

BibTeX Reference
@incollection{Hsiao_2013_7413,
   author = "Edward Hsiao and Martial Hebert",
   editor = "Sven Dickinson and Zygmunt Pizlo",
   title = "Shape-Based Instance Detection Under Arbitrary Viewpoint",
   booktitle = "Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective",
   publisher = "Springer",
   year = "2013",
}