This document discusses computer vision and machine learning techniques for visual perception. It begins with an introduction to computer vision and how machines can be taught to understand the human perceived visual world. It describes challenges with visual perception like occlusions and noise. The bulk of the document then focuses on using deep learning approaches for computer vision tasks like activity recognition. It discusses challenges with activity recognition and concludes by considering how machine vision may continue to progress and impact the future if designed according to human visual perception and judgment.

1. Art of the Tech
Future with Machine Vision
Nazli Temur
Computer Vision Engineer @ ATP

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Outline
Computer Vision Introduction
Teaching Machines Human Perceived World.
Challenges of Visual Perception
Computer Vision through Deep Learning
Conclusion
Demo

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Computer Vision Introduction
Vision

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“Computer Vision” is about
extracting semantic information
through visual perceptions.

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Domain Examples

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How did we come here
Orthographic Projection
Perspective Projection
Photography

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Where we are..
Stereo Projection(Epipolar)
Fisheye Projection

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Where we go through
We run after an enhanced way of
capturing the visual information

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Human Machine Interaction
Teaching machines humans’
visual world.

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Judgements Perceptions+ = Thinking
can explain the past ,
understand the present,
estimate the future
Cause and Effect
[Activities/Happenings] [Visual Information]
Visual Perceptions
-detection/tracking/recognition
Video Activity Recognition
?

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It is quoted by Professor David Derkins from Harvard University: ‘’up to 90% of errors in thinking are in perception
When visual information is perceived or processed incorrectly, it cannot be matched or integrated with our other
senses.

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For a machine which is capable
of thinking like a human, the
ultimate need is ;
Because it combines our visual perception
with our judgements.
“Video Activity Recognition”

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So, for a machine what is “activity recognition” about?

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Activity Recognition

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Activity Recognition

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For machine easiest way to achieve “activity recognition” is copying
cognitive subprocess underlying our visual perception and judgements

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Artificial Intelligence
Computer Vision through Deep
Learning

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What is Deep Learning?
Deep Learning is a subfield of machine learning concerned with
algorithms inspired by the structure and function of the brain called
artificial neural networks.

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Deep Learning Examples on Art

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Is the deep learning complete ?
The knowledge about physical properties of the objects can not be gained by perception…
[Kant]

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Deep Learning Examples on Art

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So, what do we expect from deep learning (AI) ?

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or by This!

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or by This!

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Available Deep Learning
Frameworks - Tools
Software Development

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caffe
theano
torch
tensorflow
mxnet

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Challenges - Visual Perception
Computer Vision

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Calibration
We need to ensure that everybody sees
straight line straight and curvature as curve.
An example with honeybee on mobile robots is here
https://www.ncbi.nlm.nih.gov/pubmed/12009050

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Occlusions
Perspective effect
Noise
Detection Challenges

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Re-identification
Noise
Real-time Processing
Tracking Challenges

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Activity Recognition Challenges
Human is a complex & complicated.
Activities are compositional.
Environment is unconstrained.
Context impact the level of detail.
Realtime happenings require high computation

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Futurism
Concluding with Brave New
Future with Machine Vision

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Heraclitus said ‘’expect the unexpected’’
http://www.mirror.co.uk/news/uk-news/killer-robots-use-facial-recognition-11557764

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Conclusion
•Machines are getting smarter, mobile and intractable
•We create them to involve our lives , so they will be following our rules.
•If only we design them according to human visual perception and judgements.
• Deep Learning is in-directly associated with human judgement.
•Deep learning can help us better defining activities and knowing about human s
•For a better future we need to convince Computer Scientists and AI Geeks :D

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Audience
Poll Link
Reset

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39. Thank you!
Questions?Questions?
Twitter: @DahaNlr
Gmail: temur.nazli@gmail.com
Linkedin: https://www.linkedin.com/in/nazlitemur/
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