Category: Project

Posts that refer to projects, consortia or collaborations are assigned to this category.

Electron Microscopy Image Segmentation

Posted on by Katie Black

David Nam, Judith Mantell, David Bull, Paul Verkade, Alin Achim

The following work presents a graphical user interface (GUI), for automatic segmentation of granule cores and membranes, in transmission electron microscopy images of beta cells. The system is freely available for academic research. Two test images are also included. The highlights of our approach are:

  • A fully automated algorithm for granule segmentation.
  • A novel shape regularizer to promote granule segmentation.
  • A dual region-based active contour for accurate core segmentation.
  • A novel convergence filter for granule membrane verification.
  • A precision of 91% and recall of 87% is observed against manual segmentations.

Further details can be found in–

D. Nam, J. Mantell, D. Bull, P. Verkade, and A. Achim, “A novel framework for segmentation of secretory granules in electron micrographs,” Med. Image Anal., vol.18, no. 2, pp. 411–424, 2014.

granulesegmenter

 

Granule Segmenter Download (Matlab)

RGBD Relocalisation Using Pairwise Geometry and Concise Key Point Sets

Posted on by csadc

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.

Estimating Visual Attention from a head-mounted IMU

Posted on by wmayol

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

Posted on by Tilo Burghardt

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

sharks

Visual Attention Based Video Compression

Posted on by dagrafiotis

Accurate prediction of the viewer’s gaze location in a video frame has the potential to improve bit allocation, rate control, error resilience and quality evaluation in video compression. With complex contexts, such as that of broadcast football video, the potential reward is even higher given that compression and transmission of this type of content is challenging. We have developed a gaze location (visual attention) prediction system for high definition broadcast football video. The system employs Bayesian integration of bottom-up features and context specific top-down cues. The context is classified into different categories through shot classification thus allowing our model to pre-learn the task pertinence of each object category and build the top-down prior map automatically.

Q. Cheng, D. Agrafiotis, A. M. Achim, D. R. Bull, “Gaze Location Prediction for Broadcast Football Video”, IEEE Transactions on Image Processing, vol 22, no. 12, pp. 4918-4929, 2013

 

p-600x450

Optimal presentation duration for video quality assessment

Posted on by Felix

Video content distributors, codec developers and researchers in related fields often rely on subjective assessments to ensure that their video processing procedures result in satisfactory quality. The current 10-second recommendation for the length of test sequences in subjective video quality assessment studies, however, has recently been questioned. Not only do sequences of this length depart from modern cinematic shooting styles, the use of shorter sequences would enable substantial efficiency improvements to the data collection process. This project, therefore, aims to explore the impact upon viewer rating behaviour of using different length video sequences and the consequent savings that could be made in time, labour and money .

Publications:

 

Felix Mercer Moss, Ke Wang, Fan Zhang, Roland Baddeley and David R. Bull, On the optimal presentation duration for subjective video quality assessment, IEEE Transactions on Circuits and Systems for Video Technology, Volume PP, Issue 99, July 2015.

Felix Mercer Moss, Chun-Ting Yeh, Fan Zhang, Roland Baddeley and David R. Bull, Support for reduced presentation durations in subjective video quality assessment, Signal Processing: Image Communication, Volume 48, October 2016, Pages 38-49.
sampleHamster-1200x675