Vectorization of 3D-Characters by Integral Invariant Filtering of High-Resolution Triangular Meshes

Mara, Hubert; Krömker, Susanne

doi:10.1109/icdar.2013.21

Cited by 26 publications

(11 citation statements)

References 10 publications

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“…This is done by using stylized rendering to generate autograph-like 2D representations of 3D cuneiform document scans, enabling query-by-example word-spotting on 3D cuneiform documents for fast sign or word retrieval. For the extraction of cuneiform wedges from 3D-scanned manuscripts, Mara et al [26] employ an integral invariant ilter over the mesh surface and subsequently on the wedge contours to identify feature points for the internal wedge structure. Fisseler et al [15] use a watershed approach on an indentation depth ield to extract robust wedge features in form of a formalized geometrical wedge model.…”

Section: Computer Aided Cuneiform Analysismentioning

confidence: 99%

“…This includes methods that process vectorized cuneiform, with the intermediate step of skeletonization. They use autograph-like 2D vector drawings generated from 3D-scanned cuneiform meshes [26] or raster images [27], to decompose vectorized cuneiform signs into wedge features, including the deinition of sign-level similarity measures [3] or decompose spline drawings into single wedges and cuneiform signs based on part-structured models [5], and convert the vector drawings into graph representations of cuneiform signs [4]. However, neither of these methods can provide a solution for the cuneiform classiication problem on photographic reproductions.…”

Section: Computer Aided Cuneiform Analysismentioning

confidence: 99%

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Illumination-based Augmentation for Cuneiform Deep Neural Sign Classification

Rest

Fisseler

Weichert

et al. 2022

J. Comput. Cult. Herit.

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Automated content-based search for arbitrary cuneiform signs in photographic reproductions is a challenging task in the analysis of ancient documents, a central component of which is a reliable cuneiform sign classification. We present an illumination-based approach to generate synthetic training data for cuneiform sign classification via deep neural networks to overcome common issues with the transferability of machine learning training results. Starting from an analysis of the negative impact of illumination variations in the processed cuneiform data, we employ an illumination augmentation to 2D training data generated from annotated 3D datasets. We demonstrate that our method is able to overcome the high visual variance of most digitized 2D cuneiform reproductions and achieve an illumination invariant generalization. The effectiveness of our approach is evaluated by its successful application to several subsets of a cuneiform script dataset with an originally poor transferability of mutual training results. Furthermore, we show that a sufficient sampling of the illumination space mostly removes the necessity to match the training data to specific target illumination conditions. The practical applicability of our approach is validated by applying it to a larger dataset, raising the overall classification accuracy by 4 percentage points to 90%, resulting in a classification error reduction of 28.5% when compared to results without the proposed data augmentation.

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Section: Computer Aided Cuneiform Analysismentioning

confidence: 99%

Section: Computer Aided Cuneiform Analysismentioning

confidence: 99%

Illumination-based Augmentation for Cuneiform Deep Neural Sign Classification

Rest

Fisseler

Weichert

et al. 2022

J. Comput. Cult. Herit.

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“…In previous work we presented methods homogenizing various sources of cuneiform tablets [4], [5], such as photographs, 3D scans [6] of original tablets and transcriptions created with a vector graphics editor. We transformed each representation into a 12 dimensional feature vector of keypoints of a wedge-shaped impression.…”

Section: Previous Workmentioning

confidence: 99%

Segmentation Free Spotting of Cuneiform Using Part Structured Models

Bogacz¹,

Howe

Mara³

2016

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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Cuneiform scripts constitute an immense source of information about ancient history, dating back almost four thousand years. Documents were written by imprinting wedgeshaped impressions into wet clay tablets, and current scholarly practice typically transcribes the resulting markings by hand with ink on paper. This work develops algorithmic methods for cuneiform script, combining feature extraction for cuneiform wedges with prior work on segmentation-free word spotting using part-structured models. We adapt the inkball model used for word spotting to treat wedge features as individual parts arranged in a tree structure. The geometric relationship between query and target is measured by the energy necessary to deform the tree structure. We also introduce an optimizing method for wedge feature extraction based on optimally assigning tablet structuring elements to hypothesized wedge models. Finally, we evaluate the method on a real-world dataset, and show that it outperforms the state of the art in cuneiform character spotting.

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“…Mara et al . [MKJB10, MK13] propose a transformation invariant feature vector, that is obtained by calculating the volumes of multiple concentric spheres intersecting the volume below the 3D model's surface at each point. They increase the readability of 3D scanned cuneiform tablets (Figure ), and use this signal to produce artificial illustrations of the writing (Figure ).…”

Section: Micro‐geometrymentioning

confidence: 99%

A Survey of Geometric Analysis in Cultural Heritage

Pintus

Pal

Yang

et al. 2015

Computer Graphics Forum

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We present a review of recent techniques for performing geometric analysis in cultural heritage applications. The survey is aimed at researchers in the areas of computer graphics, computer vision, and cultural heritage computing, as well as to scholars and practitioners in the cultural heritage field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation, and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives.

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Vectorization of 3D-Characters by Integral Invariant Filtering of High-Resolution Triangular Meshes

Cited by 26 publications

References 10 publications

Illumination-based Augmentation for Cuneiform Deep Neural Sign Classification

Illumination-based Augmentation for Cuneiform Deep Neural Sign Classification

Segmentation Free Spotting of Cuneiform Using Part Structured Models

A Survey of Geometric Analysis in Cultural Heritage

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