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Parallel Depth Recovery by Changing Camera Parameters
, 1992
"... A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene's image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) foca ..."
Abstract
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Cited by 63 (14 self)
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A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene's image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) focal length of the lens, and (iii) diameter of the lens aperture. The method is parallel involving simple local computations. In comparison with stereo vision and structure-frommotion methods, the correspondence problem does not arise. This method for depth-map recovery may also be used for (i) obtaining focused images (i.e. images having large depth of field) from two images having finite depth of field, and (ii) rapid autofocusing of computer controlled video cameras. 1. Introduction Here we describe a new passive ranging method which in principle is fast and involves relatively weak assumptions that are generally valid. The method is basically a generalized version of the `depth-from-focu...
Parallel Depth Recovery by Changing Camera Parameters
"... A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene’s image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) foca ..."
Abstract
- Add to MetaCart
A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene’s image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) focal length of the lens, and (iii) diameter of the lens aperture. The method is parallel involving simple local computations. In comparison with stereo vision and structure-frommotion methods, the correspondence problem does not arise. This method for depth-map recovery may also be used for (i) obtaining focused images (i.e. images having large depth of field) from two images having finite depth of field, and (ii) rapid autofocusing of computer controlled video cameras.

