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computational_photography_talk [2010-10-27 17:00] davegriffithscomputational_photography_talk [2010-11-12 08:10] davegriffiths
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-==odd things that people do with cameras== +11 November presentation at Aalto University on computer vision for computational photography course.
-or +
-==machine vision== +
-or +
-==understanding images with a computer==+
  
-as a computational photographer, why should I care about this?+Link to presentation: [[http://www.pawfal.org/dave/links/dave-computervision.pdf]]
  
-  * everything you do with digital photography derives from this field, eg +Videos:
-    - digital filtering (from ccd de-bayer algorithms upwards)  +
-    - auto focus/exposure/blah = basic algorithmic image understanding +
-    - face finding/smile detection +
  
-  * a lot of the future will come from this field +  * particle filter: [[http://blip.tv/file/4331006]] 
-    - object recognition +  * eigenface animation: [[http://blip.tv/file/2445290]] 
-    augmented reality +  * expression recognition: [[http://blip.tv/file/2830630]] 
-    realtime scene mapping  +  * 3d morphable face model: [[http://www.youtube.com/watch?v=nice6NYb_WA]] 
-(will talk of some of this later)+  * slam augmented reality: [[http://www.youtube.com/watch?v=cQdP-mspcak]]
  
-  it is a young and vibrant research area +Code: 
-    - lots going on +  demos: [[http://svn.lirec.eu/libs/magicsquares/examples/]]
-    - much to be discovered +
-    - lots of _hard_ problems to be solved+
  
-but not hard to get fun things happening yourself +Links: 
-  * as an artist you can cherry pick the easy/fun bits or use good quality prewritten libraries+  * opencv: [[http://opencv.willowgarage.com/wiki/]] 
 +  * artoolkit: [[http://www.hitl.washington.edu/artoolkit/]] 
 +  * nasa vision workbench: [[http://ti.arc.nasa.gov/tech/asr/intelligent-robotics/nasa-vision-workbench/]]
  
-what computer vision is not: 
-  * anything to do with neuroscience (a lot of people think it is) 
-  * it's all statistics! 
- 
-==example 1 : separate moving and still things in a sequence of images== 
- 
-  * 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 
- 
-==example 2 : seperate background from person in scene== 
- 
-  * 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 
- 
-==example 3 : faces== 
- 
-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 
- 
-  - recognising what is a face in an image (face finding) 
-  - recognising who is who in an image (face recognition) 
- 
-  * face finding: haar cascades, wavelets, example images 
-  * face recognition: eigenfaces, image space vs face space 
- 
-==eigenfaces== 
- 
-  * extracting information on age, gender, ethnicity, expression from a face image 
-  * modifying this information - changing a faces age, gender .... 
- 
-scary examples 
- 
-==augmented reality== 
- 
-  * marker pose estimation 
-  * rendering 3D objects in the real world 
-  * yeah yeah but you need a marker... 
- 
-==SLAM: simultaneous location and mapping== 
- 
-  * 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 
- 
-==3D cameras== 
- 
-  * RGB + depth per pixel 
-  * microsoft project natal 
- 
-solves a lot of problems, creates some interesting new ones 
- 
-how? 
-  * stereo cameras 
-  * structured light 
-  * wavefront analysis 
- 
-==where do I get some of this stuff?== 
- 
-  * 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. 
  • computational_photography_talk.txt
  • Last modified: 2010-11-12 23:38
  • by davegriffiths