The 3D Marching Lines Algorithm

Thirion, Jean-Philippe; Gourdon, Alexis

doi:10.1006/gmip.1996.0042

Cited by 83 publications

(36 citation statements)

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“…Suggestive contours [DeCarlo et al 2003;DeCarlo et al 2004] are places where occluding contours appear with a minimal change in viewpoint. Lines along ridges and valleys are formed by local extrema of surface curvature along one of the principal curvature directions [Interrante et al 1995;Thirion and Gourdon 1996;Pauly et al 2003;Ohtake et al 2004] and might be considered a generalization of sharp creases to smooth surfaces. Apparent ridges [Judd et al 2007] are a variant of ridges and valleys, which are extrema of a view-dependent curvature that takes foreshortening into account.…”

Section: Lines and Interpretationmentioning

confidence: 99%

How well do line drawings depict shape?

et al. 2009

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Figure 7) ridges/valleys and suggestive contours. Overlaid are representative "gauges" (discs revealing the surface normal) oriented on the images by people in the study, colored by how far they deviate from the ground truth. AbstractThis paper investigates the ability of sparse line drawings to depict 3D shape. We perform a study in which people are shown an image of one of twelve 3D objects depicted with one of six styles and asked to orient a gauge to coincide with the surface normal at many positions on the object's surface. The normal estimates are compared with each other and with ground truth data provided by a registered 3D surface model to analyze accuracy and precision. The paper describes the design decisions made in collecting a large data set (275,000 gauge measurements) and provides analysis to answer questions about how well people interpret shapes from drawings. Our findings suggest that people interpret certain shapes almost as well from a line drawing as from a shaded image, that current computer graphics line drawing techniques can effectively depict shape and even match the effectiveness of artist's drawings, and that errors in depiction are often localized and can be traced to particular properties of the lines used. The data collected for this study will become a publicly available resource for further studies of this type.

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Section: Lines and Interpretationmentioning

confidence: 99%

How well do line drawings depict shape?

et al. 2009

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“…We have proposed another method to compute them in [39,38], for the case of iso-intensity surfaces. Our method is based on the use of the implicit functions theorem.…”

Section: Extremal Linesmentioning

confidence: 99%

Landmark-Based Registration Using Features Identified through Differential Geometry

Pennec

Ayache

Thirion

2009

Handbook of Medical Image Processing and Analysis

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Registration of 3D medical images consists in computing the "best" transformation between two acquisitions, or equivalently, determines the point to point correspondence between the images. Registration algorithms are usually based either on features extracted from the image (feature-based approaches) or on the optimization of a similarity measure of the images intensities (intensitybased or iconic approaches). Another classification criterion is the type of transformation sought (e.g. rigid or non-rigid).In this chapter, we concentrate on feature-based approaches for rigid registration, similar approaches for non-rigid registration being reported in another set of publication [35,36]. We show how to reduce the dimension of the registration problem by first extracting a surface from the 3D image, then landmark curves on this surface and possibly landmark points on these curves. This concept proved its efficiency through many applications in medical image analysis as we will see in the sequel. This work has been for a long time a central investigation topic of the Epidaure team [2] and we can only reflect here on a small part of the research done in this area.We present in the first section the notions of crest lines and extremal points and how these differential geometry features can be extracted from 3D images. In Section 2, we focus on the different rigid registration algorithms that we used to register such features. The last section analyzes the possible errors in this registration scheme and demonstrates that a very accurate registration could be achieved.

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“…Hence there are 2 4 = 16 possible configurations which can be reduced to seven by rotational symmetry. Ambiguities are resolved using the method proposed in [15], [23]. We refer readers to these papers for more details.…”

Section: A2 Singlesubvoxelmarchmentioning

confidence: 99%

Inference of integrated surface, curve and junction descriptions from sparse 3D data

Tang

Medioni

1998

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-We are interested in descriptions of 3D data sets, as obtained from stereo or a 3D digitizer. We therefore consider as input a sparse set of points, possibly associated with certain orientation information. In this paper, we address the problem of inferring integrated high-level descriptions such as surfaces, 3D curves, and junctions from a sparse point set. While the method proposed by Guy and Medioni provides excellent results for smooth structures, it only detects surface orientation discontinuities but does not localize them. For precise localization, we propose a noniterative cooperative algorithm in which surfaces, curves, and junctions work together: Initial estimates are computed based on the work by Guy and Medioni, where each point in the given sparse and possibly noisy point set is convolved with a predefined vector mask to produce dense saliency maps. These maps serve as input to our novel extremal surface and curve algorithms for initial surface and curve extraction. These initial features are refined and integrated by using excitatory and inhibitory fields. Consequently, intersecting surfaces (resp. curves) are fused precisely at their intersection curves (resp. junctions). Results on several synthetic as well as real data sets are presented.Index Terms-Segmentation and feature extraction, integrated shape description, surface orientation discontinuity, surface and curve extremality.

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The 3D Marching Lines Algorithm

Cited by 83 publications

References 1 publication

How well do line drawings depict shape?

How well do line drawings depict shape?

Landmark-Based Registration Using Features Identified through Differential Geometry

Inference of integrated surface, curve and junction descriptions from sparse 3D data

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