or

or

as a computational photographer, why should I care about this?

• everything you do with digital photography derives from this field, eg
  1. digital filtering (from ccd de-bayer algorithms upwards)
  2. auto focus/exposure/blah = basic algorithmic image understanding
  3. face finding/smile detection

• a lot of the future will come from this field
  1. object recognition
  2. augmented reality
  3. realtime scene mapping

(will talk of some of this later)

• it is a young and vibrant research area
  1. lots going on
  2. much to be discovered
  3. lots of _hard_ problems to be solved

but not hard to get fun things happening yourself

• as an artist you can cherry pick the easy/fun bits or use good quality prewritten libraries

what computer vision is not:

• anything to do with neuroscience (a lot of people think it is)
• it's all statistics!

• frame differencing
• finding overall direction of movement

• this actually works, is robust to lighting changes, camera wobble etc
• results in useable data
• (all eyetoy games use this technique)

why it works - temporal memory is short and well defined

* subtract current image from reference image without persion * works! (for a few minutes)

lighting changes, camera auto settings, passing cars… all conspire against us.

doesn't work

_very hard problem_ seen much time and money sunk into solving this without result

faces are great for computational photographers because:

• most images have a few in them
• we all have one
• we are particually attuned to understanding them
• a lot of research has been done on this, a lot we can build on as artists

actually several problems

1. recognising what is a face in an image (face finding)
2. recognising who is who in an image (face recognition)

• face finding: haar cascades, wavelets, example images
• face recognition: eigenfaces, image space vs face space

• extracting information on age, gender, ethnicity, expression from a face image
• modifying this information - changing a faces age, gender ….

scary examples

• marker pose estimation
• rendering 3D objects in the real world
• yeah yeah but you need a marker…

• gradually building a model of the scene from a camera in realtime
• example video
• no markers needed
• lots of cool things will come from this in future

• RGB + depth per pixel
• microsoft project natal

solves a lot of problems, creates some interesting new ones

how?

• stereo cameras
• structured light
• wavefront analysis

• opencv
• artoolkit
• nasa image processing library
• lots of research code is out there and waiting for artistic (mis)use!
• they may actually help you in between publishing papers

tips

• think like a photographer - how can I set the scene to suit what I am trying to do, lights, camera position etc etc are as important as finding the right algorithm.