Show pageOld revisionsBacklinksBack to top You've loaded an old revision of the document! If you save it, you will create a new version with this data. Media Files==odd things that people do with cameras== or ==machine vision== or ==understanding images with a computer== as a computational photographer, why should I care about this? * everything you do with digital photography derives from this field, eg - 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 - object recognition - augmented reality - realtime scene mapping (will talk of some of this later) * it is a young and vibrant research area - lots going on - much to be discovered - 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! ==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.Please fill all the letters into the box to prove you're human. Please keep this field empty: SavePreviewCancel Edit summary Note: By editing this page you agree to license your content under the following license: CC Attribution-Share Alike 4.0 International computational_photography_talk.1288198544.txt.gz Last modified: 2010-10-27 16:55by davegriffiths