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Content Provider | IET Digital Library |
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Author | He, Hu Upcroft, Ben |
Abstract | This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results. |
Starting Page | 45 |
Ending Page | 53 |
Page Count | 9 |
ISSN | 17519632 |
Volume Number | 8 |
e-ISSN | 17519640 |
Issue Number | Issue 1, Feb (2014) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/8/1 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0018 |
Journal | IET Computer Vision |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Automatic Object Segmentation Camera Positions Colour Information Combinatorial Mathematics Computer Vision And Image Processing Technique Depth Information Graph Cuts Optimisation Graph Theory Image Colour Analysis Image Recognition Image Segmentation Image Sequence Manual Intervention Markov Processes Markov Random Field Mobile Robots Monocular Video Sequence Object Recognition Optimisation Optimisation Technique Outdoor Robotic Application Pixel Seed Quadcopter Segmentation Pipeline Standard Segmentation Routines Unstructured Scenes Video Signal Processing |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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