Conformal curvature flows: From phase transitions to active vision

Kichenassamy, Satyanad; Kumar, Arun; Olver, Peter J.; Tannenbaum, Allen; Yezzi, Anthony

doi:10.1007/bf00379537

Cited by 373 publications

(316 citation statements)

References 49 publications

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“…The column on the right of Fig. 1 shows better results when 'P i s adaptively computed for each I P u sing (13). This modeling prevents oversmoothing of edges.…”

Section: Resultsmentioning

confidence: 96%

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A level line selection approach for object boundary estimation

Kervrann

Hoebeke

Trubuil

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

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“…The column on the right of Fig. 1 shows better results when 'P i s adaptively computed for each I P u sing (13). This modeling prevents oversmoothing of edges.…”

Section: Resultsmentioning

confidence: 96%

“…Geodesic active contours [13,7] based on the theory of surfaces evolution and geometric flows have been introduced to detect an arbitrary number of objects in the image. These models precisely delineate object boundaries but can rely on unreliable local information to make a decision.…”

Section: Introductionmentioning

confidence: 99%

A level line selection approach for object boundary estimation

Kervrann

Hoebeke

Trubuil

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

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“…10 and 11. Related level set based active contours are, in particular, edge based geodesic active contours (12,13) and region based active contours, as introduced by Chan and Vese (14) and Paragios and Deriche (15).…”

Section: Segmentation Methodsmentioning

confidence: 99%

Fully automated segmentation and morphometrical analysis of muscle fiber images

et al. 2007

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Background: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylineosin stained cryostat sections. Methods: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass.

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“…more sophisticated techniques can be used. Here, we extracted the contour with the active contour algorithm as formulated in Caselles et al (1997), Kichenassamy et al (1996). 7 The resulting medial axes are shown in Figs.…”

Section: Examplesmentioning

confidence: 99%

Area-Based Medial Axis of Planar Curves

Niethammer

Betelú

Sapiro

et al. 2004

International Journal of Computer Vision

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A new definition of affine invariant medial axis of planar closed curves is introduced. A point belongs to the affine medial axis if and only if it is equidistant from at least two points of the curve, with the distance being a minimum and given by the areas between the curve and its corresponding chords. The medial axis is robust, eliminating the need for curve denoising. In a dynamical interpretation of this affine medial axis, the medial axis points are the affine shock positions of the affine erosion of the curve. We propose a simple method to compute the medial axis and give examples. We also demonstrate how to use this method to detect affine skew symmetry in real images.

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Conformal curvature flows: From phase transitions to active vision

Cited by 373 publications

References 49 publications

A level line selection approach for object boundary estimation

A level line selection approach for object boundary estimation

Fully automated segmentation and morphometrical analysis of muscle fiber images

Area-Based Medial Axis of Planar Curves

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