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The Natural Bipeds, Birds and Humans: An Inspiration for Bipedal Robots Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Towards Unsupervised Canine Posture Classification via Depth Shadow Detection and Infrared Reconstruction for Improved Image Segmentation Accuracy

Authors : Sean Mealin, Steven Howell, David L. Roberts

Published in: Biomimetic and Biohybrid Systems

Publisher: Springer International Publishing

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Abstract

Hardware capable of 3D sensing, such as the Microsoft Kinect, has opened up new possibilities for low-cost computer vision applications. In this paper, we take the first steps towards unsupervised canine posture classification by presenting an algorithm to perform canine-background segmentation, using depth shadows and infrared data for increased accuracy. We report on two experiments to show that the algorithm can operate at various distances and heights, and examine how that effects its accuracy. We also perform a third experiment to show that the output of the algorithm can be used for k-means clustering, resulting in accurate clusters 83 % of the time without any preprocessing and when the segmentation algorithm is at least 90 % accurate.

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previous chapter A Neural Network with Central Pattern Generators Entrained by Sensory Feedback Controls Walking of a Bipedal Model
next chapter A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot
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Metadata
Title
Towards Unsupervised Canine Posture Classification via Depth Shadow Detection and Infrared Reconstruction for Improved Image Segmentation Accuracy
Authors
Sean Mealin
Steven Howell
David L. Roberts
Copyright Year
2016
Book
Biomimetic and Biohybrid Systems

Print ISBN: 978-3-319-42416-3

Electronic ISBN: 978-3-319-42417-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42417-0_15

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