ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Grey, Black and Cyan
Theme name
Vision for action and active vision
Members
Nicola Bellotto, Andy Foulkes, David Gibson, Iain Gilchrist, Paul Graham, Frank Guerin, Nick Hockings,
Alan Johnston, Dmitry Kit, Casmir Ludwig, Andrew Philippides, Simon Rushton, Jeremy Wyatt,
Johannes Zanker
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
(2)
(3)
(4)
(5)
(6)
(1) Real time vision
Radically different bio-inspired architectures for robotics (hardware and software)
The role of other sensing modalities (including e.g. touch for robotics)
The vision for action and action for vision loop
How to specify and schedule the task for visually guided action and behaviour
The problem of manipulation in robotics
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
1. Vision as a mobile platform
2. Active Vision
Sampling + structureing vision
Active Simultaneous Location and Mapping (SLAM) – Computer Vision
Info Max – Biological
3. Vision for Action
Goal directed
Navigation
Manipulation
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Red and Yellow - 1
Theme name
Plasticity and modelling
Members
Wendy Adams, Roland Fleming, Ross Goutcher, Sophie Wurger, David Foster, Aaro Sloman, Sepehr
Jalali, Ahmed Al-Baldi, N V Kartheek Medatathi, Miles Hansard, Andrew Parker, Kun Giu
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
Perceptual Learning / Plasticity – Equivalents in machine systems
Level of Models
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
Targeted review for counterpart in “other camp”.
Importance of being honest about what we do and do not know.
Utilitarian / behaviour oriented concepts of vision.
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
Frustrations: “Biological Motivation” is shallow and often simply and excuse for poor performance,
mediocrity.
Question: Are neurons per se important for computation?
Where to publish. “Falling between the chairs”
Special issues with joint reviews – consensus between reviewers from different fields.
ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Red and Yellow - 2
Theme name
Objects, 3D, Recognition
Members
Max Di Luca, Andrew Glennester, Peter Scarfe, David Hunter, Paul Hibbard, Alex Murry, Sonya
Coleman, Dermot Kerr, Arnlod Wilkins, Andrew Welchman, Simon Watt, Paul Hand, Toby Breckon,
Shufan Yang, George Lovell
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
Deep Learning, Object recognition – Converging towards “difficult” tasks (i.e., high regularity in the
space of object recognition), Bayesian implementation in neurons.
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
Parallelism in processing information
Natural scene statistics
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
- Improve speed in computer vision systems using biological mechanisms. I.e. for autonomous
vehicles
- Efficiency; how is it defined in the two areas, how is it measured.
-Make labs form different backgrounds work on same topic. I.e., to identify some specific tasks that
can be tackled from the two directions.
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
- Feedback from computer vision to biological models and viceversa.
- How to identify the biological models to implement in large-scale computer projects (HBP, Blue
brain).
- Computer vision solves “low level” problems and biological vision solves “large scale” problems but
it’s challenging to find overlaps.
-Openness of data bases and results / open science.
-Different goals in computer vision and biological vision research
- What are the underlying representations?
ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Pink and Purple
Theme name
Better Together (Augmenting vision, learning and other topics)
Members
John Solomon, Daniel Baker, David Tolhurst, Manos Protonotarios, Craig Henderson, Mark van
Rossum, Xiaohng Gao, Ruixuan Wangm Farzin Deravi, Nicolas Pugeault, Zyg Pizlo, Keith May, Thomas
Tanay, Sander Keemink, Jonas Kubilius, Brian Barsky, Elena Gheorghiu, Zhaoping Li, Cristina Hilario
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
Multi-electrode recording
Big data,
Optogenetics,
Materials perception
Deep networks
Learning linking to machine vision
Fundamental differences between fovea and periphery
Attention
Future hot topic using computers to augment human vision
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
Difference may be a good thing
Augmentation of low vision
Edge detection
Segmentations
Statistics of signals
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Orange & Green
Theme name
Attention and scene understanding
Members
Darren Cosker, Eva Krumhuber, Dietmar Heinke, Anna Belardinelli, Jiangning Gao, Nick Costen, Jason
Vanderventner, Isabelle Mareschal, Ales Leonardis, Tom Foulsham, Richard Bowden, Charles Leek
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
Context: whats important in a scene
- How visual system selects / extracts
- Describes which information to use to make decision about scene.
- Top down / Bottom up
- Egocentric capture
- Type of stimulus / Scene, colour
- How we extract information from complex scenes / simple stimuli
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
- Create model of scene& synthesise new ones
- Attention, where to look in a scene
- How biological systems & machine systems help each other
-Plausible model of biological system what canbe tested by machine vision.
-Building intelligent models / realism
-Priors.
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
- Grand challeng (50 years) blade runner.
-What do humans attend to? / How we understand interaction.
-Understand basis of attention / cognitions / Navigation.
-Go beyond very small defined areas – vision systems normally studies in very small constrained way
(small areas).
- How info is put together (humans are good at integrating information) subtle cues, ways of finding
ground truth.
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
-Black box: not most efficient system if too aim for perfect system.
- What are rhe most fundamental components.
- What’s the metric of performance (accuracy, speed)?
- Scalability, how to model large number of categories.
- Different types of methods, can work in incremental manner (functional level).
ViiHM First Workshop – Landscaping exercise – Theme reports
Group colours
Blue and Brown
Theme name
Colour and appearance
Members
Stephen Pollard, Frederic Fol Leymarie, Han Gong, Jansa Matinovic, Bougslaw Obara, Chas Nelson,
Kazda Xiao, Tushar Chauhan, Rafel Mantiuk
Please address the following questions on a flip chart putting your group colours and theme name
on the chart. Add notes here also.
Current hot topics:
What’s currently exciting in this area - what is generating a buzz?
- 3D printing - > colour management.
- Colour in 3D -> luminance role in colour perception.
- Appearance as multisensory problem.
- Influence of higher – Cognition, - Emotion, - Culture
Overlaps:
What overlaps between biological and computer vision are taking place?
Can you identify any potential overlap areas?
Conflict between:
Simplify to measure / study
Personalise to perform
Aims:
What would your group expect or like to see happen within the theme in the next 5 years?
Can you identify a single ‘challenge’ topic with an overlap between biological and machine vision
that might be addressed in the next 5 years?
Challenges:
Is there anything restricting collaboration in this area?
What challenges might old back progress in this area?
- Language not shared
- Taking time to speak to colleagues just to understand other areas.