Search Results - "Zucker, S.W."

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  1. 1

    Indexing hierarchical structures using graph spectra by Shokoufandeh, A., Macrini, D., Dickinson, S., Siddiqi, K., Zucker, S.W.

    “…Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution…”
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    Journal Article
  2. 2

    Greedy Basis Pursuit by Huggins, P.S., Zucker, S.W.

    Published in IEEE transactions on signal processing (01-07-2007)
    “…We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries. GBP is rooted in…”
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    Journal Article
  3. 3

    Matching hierarchical structures using association graphs by Pelillo, M., Siddiqi, K., Zucker, S.W.

    “…It is well-known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a (derived)…”
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    Journal Article
  4. 4

    The perceptual organization of texture flow: a contextual inference approach by Ben-Shahar, O., Zucker, S.W.

    “…Locally parallel dense patterns-sometimes called texture flows-define a perceptually coherent structure of particular significance to perceptual organization…”
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    Journal Article
  5. 5

    Sketches with curvature: the curve indicator random field and Markov processes by August, J., Zucker, S.W.

    “…While complaints about typical edge operators are common, proposals articulating a notion of the "perfect" edge map are comparatively rare, hindering the…”
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    Journal Article
  6. 6

    Area and length minimizing flows for shape segmentation by Siddiqi, K., Lauziere, Y.B., Tannenbaum, A., Zucker, S.W.

    Published in IEEE transactions on image processing (01-03-1998)
    “…A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation,…”
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    Journal Article
  7. 7

    Trace inference, curvature consistency, and curve detection by Parent, P., Zucker, S.W.

    “…An approach is described for curve inference that is based on curvature information. The inference procedure is divided into two stages: a trace inference…”
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    Journal Article
  8. 8

    On the perceptual organization of texture and shading flows: from a geometrical model to coherence computation by Ben-Shahar, O., Zucker, S.W.

    “…Locally parallel dense patterns - sometimes called texture flows define a perceptually coherent structure which is important to image segmentation, edge…”
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    Conference Proceeding
  9. 9

    Audio-Visual Group Recognition Using Diffusion Maps by Keller, Y., Coifman, R.R., Lafon, S., Zucker, S.W.

    Published in IEEE transactions on signal processing (01-01-2010)
    “…Data fusion is a natural and common approach to recovering the state of physical systems. But the dissimilar appearance of different sensors remains a…”
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    Journal Article
  10. 10

    Differential Geometric Inference in Surface Stereo by Gang Li, Zucker, S.W.

    “…Many traditional two-view stereo algorithms explicitly or implicitly use the frontal parallel plane assumption when exploiting contextual information since,…”
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    Journal Article
  11. 11

    Local scale control for edge detection and blur estimation by Elder, J.H., Zucker, S.W.

    “…We show that knowledge of sensor properties and operator norms can be exploited to define a unique, locally computable minimum reliable scale for local…”
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    Journal Article
  12. 12

    constant production hypothesis guides leaf venation patterning by Dimitrov, P, Zucker, S.W

    “…We propose a theoretical mechanism that enables the elaboration of veins to supply distant cells during leaf development. In contrast to the more standard view…”
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    Journal Article
  13. 13

    Singularities of principal direction fields from 3-D images by Sander, P.T., Zucker, S.W.

    “…Generic singularities can provide position-independent information about the qualitative shape of surfaces. The authors determine the singularities of the…”
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    Journal Article
  14. 14

    Indexing using a spectral encoding of topological structure by Shokoufandeh, A., Dickinson, S.J., Siddiqi, K., Zucker, S.W.

    “…In an object recognition system, if the extracted image features are multilevel or multiscale, the indexing structure may take the form of a tree. Such…”
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    Conference Proceeding
  15. 15

    Surface Geometric Constraints for Stereo in Belief Propagation by Gang Li, Zucker, S.W.

    “…Belief propagation has been shown to be a powerful inference mechanism for stereo correspondence. However the classical formulation of belief propagation…”
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    Conference Proceeding
  16. 16

    Logical/linear operators for image curves by Iverson, L.A., Zucker, S.W.

    “…We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify…”
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    Journal Article
  17. 17

    On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis by Savadjiev, P., Zucker, S.W., Siddiqi, K.

    “…Configurations of dense locally parallel 3D curves occur in medical imaging, computer vision and graphics. Examples include white matter fibre tracts,…”
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    Conference Proceeding
  18. 18

    The Hamilton-Jacobi skeleton by Siddiqi, K., Bouix, S., Tannenbaum, A., Zucker, S.W.

    “…The eikonal equation and variants of it are of significant interest for problems in computer vision and image processing. It is the basis for continuous…”
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    Conference Proceeding
  19. 19

    Hue fields and color curvatures: a perceptual organization approach to color image denoising by Ben-Shahar, O., Zucker, S.W.

    “…The denoising of color images is an increasingly studied problem whose state-of-the-art solutions employ a variety of diffusion schemes. Specifying the correct…”
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    Conference Proceeding
  20. 20

    Shadows and shading flow fields by Breton, P., Zucker, S.W.

    “…Many presume that parsing the shadows out of an image is a high-level task, because of the global nature of the shadow formation process. But…”
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    Conference Proceeding