Download 3-D shape estimation and image restoration : exploiting by Paolo Favaro PDF

By Paolo Favaro

In the components of picture processing and laptop imaginative and prescient, there's a specific desire for software program which could, given an unfocused or motion-blurred picture, infer the three-d form of a scene. This ebook describes the analytical tactics that cross into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in snapshot processing and desktop imaginative and prescient and with backgrounds in engineering, technological know-how or arithmetic, this hugely functional text/reference is obtainable to complex scholars or people with a level that incorporates uncomplicated linear algebra and calculus courses.

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Extra resources for 3-D shape estimation and image restoration : exploiting defocus and motion blur

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18) k=0 then we say that the surface s has a degree of resolution ρ. The above definition of degree of resolution for the radiance r and the surface s depends on the choice of orthonormal basis {θk }. One could make the definition independent of the choice of basis by defining ρ as the minimum integer with respect to the choice of basis in a class. Alternatively, one can just choose the same basis for both definitions, in particular when comparing different surfaces and different radiances as we show below.

39) 32 2. 9 This is equivalent to averaging over a “soft temporal window” rather than in between the idealized opening and closing of the shutter, because in reality the shutter, under the auspices of inertia, opens and closes gradually. The constant κ may be necessary to match ΔT˜ with the nominal shutter interval indicated in the camera specifications. Now consider a scene composed of Y objects moving independently with velocities ν1 , . . , νY . Y the regions of the image plane occupied by the projections of each of the moving objects.

In fact, if we let the aperture D become infinitesimal, light travels from a point on the scene through the “pinhole” aperture onto a unique point on the image plane. In this case we have simply that I(y) = r(x), where x is a point on the ray through y and the origin, where the pinhole is located. This pinhole imaging model is clearly an idealization, because diffraction effects become dominant when the aperture decreases, and in the limit where the aperture becomes zero no light can pass through it.

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