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1 Algorithms compared Bicubic Interpolation Mitra's Directional Filter Fuzzy Logic Filter Vector Quantization VISTA.

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Presentation on theme: "1 Algorithms compared Bicubic Interpolation Mitra's Directional Filter Fuzzy Logic Filter Vector Quantization VISTA."— Presentation transcript:

1 1 Algorithms compared Bicubic Interpolation Mitra's Directional Filter Fuzzy Logic Filter Vector Quantization VISTA

2 2 Bicubic splineAltamiraVISTA

3 3 Bicubic splineAltamiraVISTA

4 4 User preference test results The observer data indicates that six of the observers ranked Freemans algorithm as the most preferred of the five tested algorithms. However the other two observers rank Freemans algorithm as the least preferred of all the algorithms…. Freemans algorithm produces prints which are by far the sharpest out of the five algorithms. However, this sharpness comes at a price of artifacts (spurious detail that is not present in the original scene). Apparently the two observers who did not prefer Freemans algorithm had strong objections to the artifacts. The other observers apparently placed high priority on the high level of sharpness in the images created by Freemans algorithm.

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7 7 Training images

8 8 Training image

9 9 Processed image

10 10 Inference in Markov Random Fields Gibbs sampling, simulated annealing Iterated conditional modes (ICM) Belief propagation Application examples: super-resolution motion analysis shading/reflectance separation Graph cuts Variational methods

11 11 Motion application image patches image scene scene patches

12 12 What behavior should we see in a motion algorithm? Aperture problem Resolution through propagation of information Figure/ground discrimination

13 13 The aperture problem

14 14 The aperture problem

15 15 Motion analysis: related work Markov network –Luettgen, Karl, Willsky and collaborators. Neural network or learning-based –Nowlan & T. J. Senjowski; Sereno. Optical flow analysis –Weiss & Adelson; Darrell & Pentland; Ju, Black & Jepson; Simoncelli; Grzywacz & Yuille; Hildreth; Horn & Schunk; etc.

16 16 Motion estimation results (maxima of scene probability distributions displayed) Initial guesses only show motion at edges. Iterations 0 and 1 Inference: Image data

17 17 Motion estimation results Figure/ground still unresolved here. (maxima of scene probability distributions displayed) Iterations 2 and 3

18 18 Motion estimation results Final result compares well with vector quantized true (uniform) velocities. (maxima of scene probability distributions displayed) Iterations 4 and 5

19 19 Inference in Markov Random Fields Gibbs sampling, simulated annealing Iterated conditional modes (ICM) Belief propagation Application examples: super-resolution motion analysis shading/reflectance separation Graph cuts Variational methods

20 Forming an Image Surface (Height Map) Illuminate the surface to get: The shading image is the interaction of the shape of the surface and the illumination Shading Image

21 Painting the Surface Scene Add a reflectance pattern to the surface. Points inside the squares should reflect less light Image

22 Problem What if we want to do shape from shading? Need some mechanism of informing the shape from shading algorithm about the reflectance pattern. Image Height Map

23 Represent Scene As These types of images are known as intrinsic images (Barrow and Tenenbaum) = Shading Image Image Reflectance Image

24 Not Just for Shape from Shading Ability to reason about shading and reflectance independently is necessary for most image understanding tasks –Material recognition –Image segmentation Intrinsic images are a convenient representation of the scene –Less complex than fully reconstructing the scene

25 Goal Image Shading Image Reflectance Image


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