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Bio-mimetic Control Research Center, RIKEN Guided learning from images using an uncertain granular model and bio-mimicry of the human fovea Jonathan Rossiter.

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Presentation on theme: "Bio-mimetic Control Research Center, RIKEN Guided learning from images using an uncertain granular model and bio-mimicry of the human fovea Jonathan Rossiter."— Presentation transcript:

1 Bio-mimetic Control Research Center, RIKEN Guided learning from images using an uncertain granular model and bio-mimicry of the human fovea Jonathan Rossiter Toshiharu Mukai Institute of Physical and Chemical Research RIKEN, Japan + University of Bristol

2 Bio-mimetic Control Research Center, RIKEN 2 Bio-mimetic AI Studying and copying human intelligence and behaviours in artificially intelligent systems Perceptions and sensing Representations Reasoning Learning Adapting and updating

3 Bio-mimetic Control Research Center, RIKEN 3 Motivation: human-like robotics Rescue Robot Hazardous Real-time/on-site training Remote control Autonomous Intelligence Guided operation Dumb Guided learning Dumb, but at least its learning…

4 Bio-mimetic Control Research Center, RIKEN 4 Consider only image domain Learning from image data (goal is high level model) Crisp image data Conventional features Crisp values But what is uncertain image data? High level concepts encroaching on low level data Degrees of applicability/relevance across larger scale features So need to combine both crisp image data and uncertain image data crisp image data induction uncertainty model uncertain image data induction uncertainty model

5 Bio-mimetic Control Research Center, RIKEN 5 Learning with granules Size(P) = { small : 0.3, medium : 0.7} Cost(P) = { reasonable : 0.2, cheap : 0.8} G P = {small^reasonable: 0.3*0.2, small^cheap: 0.3*0.8, medium^reasonable: 0.7*0.2, medium^ cheap: 0.7*0.8 }

6 Bio-mimetic Control Research Center, RIKEN 6 Learning with granules A granule is thus a discrete fuzzy set G over the universe of cross-product labels L: G = {l L : m [0, 1]} where: L =×(K i | i = 1,..., n) and K i is a single fuzzy set label (e.g. small, medium, etc) In this paper the aggregation operation used to turn training instances G j into the model G M is simply : G M = Norm( j G j ) And with applicability values this becomes: G M = Norm( j G j × a j )

7 Bio-mimetic Control Research Center, RIKEN 7 Human visual system – from light to electricity

8 Bio-mimetic Control Research Center, RIKEN 8 Light sensors in the retina

9 Bio-mimetic Control Research Center, RIKEN 9 Vision and active learning

10 Bio-mimetic Control Research Center, RIKEN 10

11 Bio-mimetic Control Research Center, RIKEN 11 Fovea- based region focus ApplicabilityFovea scaling RelativeAbsolute

12 Bio-mimetic Control Research Center, RIKEN 12

13 Bio-mimetic Control Research Center, RIKEN 13 Unit applicability 34.6% Relative scale Gaussian-type applicability ( = 0.3) 49.5% Absolute scale Gaussian-type applicability ( = 0.4 over 5%) 48.9%

14 Bio-mimetic Control Research Center, RIKEN 14 Trapezoidal applicability x = % Relative scale Gaussian-type applicability ( = 0.2) 83.9% Unit applicability 82.1%

15 Bio-mimetic Control Research Center, RIKEN 15 Conclusions Fovea-like applicability functions better Natural Incorporates into linguistic inductive learning Not clear whether relative or absolute functions are better But, with relative applicability we need not worry about absolute scale. Easier. Further research Optimize the choice of applicability function Incorporating such a system into tools to aid medical diagnosis and into vision systems for rescue robots operating in hazardous environments.

16 Bio-mimetic Control Research Center, RIKEN 16 Thank you

17 Bio-mimetic Control Research Center, RIKEN 17 Image feature scale

18 Bio-mimetic Control Research Center, RIKEN 18 High level + low level information Low level Sensor based Data rich Crisp/precise High level Taxononomical Conceptual Linguistic Uncertain Fusion in training High + low best of both worlds

19 Bio-mimetic Control Research Center, RIKEN 19 Updating robot vision Human guidance of robot Varied environments

20 Bio-mimetic Control Research Center, RIKEN 20 Human-like perception Goldberg and terminus of perception Image features in abstract to terminus Kind of high-level from low level Also have high level information Modifies/constrains our views of image data Examples Humans combine both high and low level image information Good place to look for inspiration Bio-mimetic high level approaches to reasoning with information and sensor data


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