We describe a novel RGBD relocalisation algorithm based on key point matching. It combines two com- ponents. First, a graph matching algorithm which takes into account the pairwise 3-D geometry amongst the key points, giving robust relocalisation. Second, a point selection process which provides an even distribution of the ‘most matchable’ points across the scene based on non-maximum suppression within voxels of a volumetric grid. This ensures a bounded set of matchable key points which enables tractable and scalable graph matching at frame rate. We present evaluations using a public dataset and our own more difficult dataset containing large pose changes, fast motion and non-stationary objects. It is shown that the method significantly out performs state-of- the-art methods.
Month: February 2016
Estimating Visual Attention from a head-mounted IMU
We are developing methods for the estimation of both temporal and spatial visual attention using a head-worn inertial measurement unit (IMU). Aimed at tasks where there is a wearer-object interaction, we estimate the when and the where the wearer is interested in. We evaluate various methods on a new egocentric dataset from 8 volunteers and compare our results with those achievable with a commercial gaze tracker used as ground-truth. Our approach is primarily geared for sensor-minimal EyeWear computing.
From the paper:
Teesid Leelasawassuk, Dima Damen, Walterio W Mayol-Cuevas, Estimating Visual Attention from a Head Mounted IMU. ISWC ’15 Proceedings of the 2015 ACM International Symposium on Wearable Computers. ISBN 978-1-4503-3578-2, pp. 147–150. September 2015.
http://www.cs.bris.ac.uk/Publications/Papers/2001754.pdf
http://dl.acm.org/citation.cfm?id=2808394&CFID=548041087&CFTOKEN=31371660
Automated Fin Identification of Individual Great White Sharks
The objective of this work is automatically to identify individual great white sharks in a database of thousands of unconstrained fin images. The approach put forward appreciates shark fins in natural imagery as smooth, flexible and partially occluded objects with an individuality encoding trailing edge.
REFERENCES:
– Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery
Hughes, B. & Burghardt, T. 2015 Proceedings of the 26th British Machine Vision Conference (BMVC). British Machine Vision Association, p. 92.1-92.14
– Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery
Hughes, B. & Burghardt, T. 2015, Machine Vision of Animals and their Behaviour Workshop at BMVC, British Machine Vision Association, p. 8.1-8.8