Get 3-D Shape Estimation and Image Restoration: Exploiting PDF

By Paolo Favaro

ISBN-10: 0387279431

ISBN-13: 9780387279435

ISBN-10: 1846281768

ISBN-13: 9781846281761

ISBN-10: 1846286883

ISBN-13: 9781846286889

Within the components of snapshot processing and computing device imaginative and prescient, there's a specific want for software program that may, given an unfocused or motion-blurred picture, infer the three-d form of a scene. This e-book describes the analytical methods that move into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in photograph processing and machine 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 simple linear algebra and calculus classes.

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Additional resources for 3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur

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Y . Y the regions of the image plane occupied by the projections of each of the moving objects. Y Y Ωi = ∅ for is a partition of Ω, that is, that Ω = j=1 Ωj and that Ωj ∀ i, j = 1 . . Y , i = j. This is because the “background” scene can be considered to be an object itself. In this case, the image model becomes I(y) = √ 2 1 ˜2 e−t /(2ΔT ) r(y + νj (y, t))dt 2πΔT˜ ∀ y ∈ Ωj . 40) In order to complete the model we need to determine the velocities νj , which are a function of the shape and motion of objects in the scene.

Motion blur. Objects moving faster than the shutter appear motion blurred. The amount of motion blur depends on the shutter speed as well as the objects speed. 8). To derive a first simple model of the formation of a motion-blurred image, assume that the scene is made of a single rigid object,and the camera moves relative to it. This generates a relative image motion which we call ν and that we characterize later in the section. For now, ν : Ω × [0, ∞) → R2 is a two-dimensional function (field) of the pixel coordinate y and time t.

19) k=0 then I(s1 |r) ⊃ s2 γk (y, v, s1 ) = γk (y, v, s2 ), ∀y ∈ Ω, v ∈ V, k = 0 . . ρ . 20) Proof. 20). 10. The practical value of the last proposition is to state that, the more “irregular” the radiance, the more resolving power it has. When we can project structured light on the scene, the proposition establishes that “high-frequency” patterns should be used in conjunction with shape from defocus. An “infinitely irregular” pattern should therefore be universally exciting, as conjectured in the previous section.

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3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur by Paolo Favaro

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