Prominent field for shape processing of archaeological artifacts

Kolomenkin, Michael; Shimshoni, Ilan; Tal, Ayellet

doi:10.1109/iccvw.2009.5457604

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…In [33] we propose another framework for enhancing artifacts. It is based on a definition of a new smooth direction field, termed the prominent field, defined for every point on the surface.…”

Section: Coloringmentioning

confidence: 99%

3D Shape Analysis for Archaeology

Tal

2014

Lecture Notes in Computer Science

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Abstract. Archaeology is rapidly approaching an impasse in its ability to handle the overwhelming amount and complexity of the data generated by archaeological research. In this paper, we describe some results of our efforts in developing automatic shape analysis techniques for supporting several fundamental tasks in archaeology. These tasks include documentation, looking for corollaries, and restoration. We assume that the input to our algorithms is 3D scans of archaeological artifacts. Given these scans, we describe three techniques of documentation, for producing 3D visual descriptions of the scans, which are all non-photorealistic. We then proceed to explain our algorithm for partial similarity of 3D shapes, which can be used to query databases of shape, searching for corollaries. Finally, within restoration, we describe our results for digital completion of broken 3D shapes, for reconstruction of 3D shapes based on their line drawing illustrations, and for restoration of colors on 3D objects. We believe that when digital archaeological reports will spread around the globe and scanned 3D representations replace the 2D ones, our methods will not only accelerate, but also improve the results obtained by the current manual procedures.

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Section: Coloringmentioning

confidence: 99%

3D Shape Analysis for Archaeology

Tal

2014

Lecture Notes in Computer Science

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“…Parabolic lines correspond to the zeros of the Gaussian and mean curvature, respectively. Finally, demarcating curves are the zero-crossings of the curvature in its gradient direction [42,41]. In general, all these curves, defined as the zero set of a scalar function, do not consider curve with knots, fact that an algebraic curve like the Cartesian Folium (see [61]) could arrange.…”

Section: Feature Curves Identificationmentioning

confidence: 99%

Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms

Torrente

Biasotti

Falcidieno

2018

Pattern Recognition

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Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and/or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem.Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves.

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“…Only a few existing techniques have tackled this problem. The prominent field technique [KST09b] combines output of second‐ and third‐order tensors to identify a direction field along which prominent relief features are likely to be located. This direction field has proven to be useful for feature‐aware smoothing, curvature‐based shading, and more recently line‐based rendering [KST09a].…”

Section: Previous Workmentioning

confidence: 99%

Surface Relief Analysis for Illustrative Shading

Ammann¹,

Barla²,

Guennebaud³

et al. 2012

Computer Graphics Forum

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In this paper, we present an analysis technique that leverages the complexity found in detailed 3D models for illustrative shading purposes. Given a smooth base surface with relief, it locates relief features (concavities, convexities and inflections) around each surface point and at multiple scales, using cubic‐polynomial fitting. This object‐space, per‐vertex information is then used to guide a variety of shading techniques including normal enhancement, feature visualization, accessibility shading and radiance scaling. Thanks to this approach, features at multiple scales are easily combined, filtered and shaded, allowing users to explore surface relief in real‐time.

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Prominent field for shape processing of archaeological artifacts

Cited by 6 publications

References 26 publications

3D Shape Analysis for Archaeology

3D Shape Analysis for Archaeology

Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms

Surface Relief Analysis for Illustrative Shading

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