Relief Extraction from Stone Monuments

Heo, Eui-Ju; Sa, Jae-Min; Choi, Kang-Sun

doi:10.5573/ieiespc.2018.7.4.321

Cited by 3 publications

(12 citation statements)

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“…Another approach to automatic relief extraction exploits the curvatures of the 3D data [6], [9]. Lawonn et al [6] presented the curvature-based relief extraction method (CRE) by adapting the Frangi filter for extracting reliefs from the 3D data.…”

Section: Related Workmentioning

confidence: 99%

“…In the modified curvature-based relief extraction method (MCRE) [9], such problems are alleviated by nullifying the vesselness values smaller than a threshold T V to zero and deactivating the saliency-based selection. We utilize MCRE for initial relief candidate segment extraction.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the performance of the proposed method, we compared it with DRE [4], CRE [6], and MCRE [9]. We separated the 70 characters into the training set of 61 characters and the test set of 9 characters.…”

Section: A Evaluationmentioning

confidence: 99%

“…9 illustrates the results of the various relief extraction methods for subjective [4] in (b) yielded unsatisfactory results due to undetected strokes or many thick noise detection. CRE [6] in (c) and MCRE [9] in (d) are better than DRE, but there are still a lot of falsely detected noises. The proposed method in (e) produced very pleasing results similar to the ground truth in (f) by selecting and removing noise segments successfully.…”

Section: A Evaluationmentioning

confidence: 99%

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Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Evaluationmentioning

confidence: 99%

Section: A Evaluationmentioning

confidence: 99%

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Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

et al. 2021

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“…Following the successful application of the Frangi filter in the detection of blood vesselness [17], a modified Frangi filter was developed to extract relief details from the surface of 3D mesh [7,18]. Considering the canal-like shape of the reliefs, curvatures are intuitive and effective measures for detecting reliefs.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method Based on 3D Local Surface Representation for Recognizing the Inscriptions on Ancient Stele

Murtala

Choi

2021

Applied Sciences

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It is challenging to extract reliefs from ancient steles due to their rough surfaces which contain relief-like noise such as dents and scratches. In this paper, we propose a method to segment relief region from 3D scanned ancient stele by exploiting local surface characteristics. For each surface point, four points that are apart from the reference point along the direction of the principal curvatures of the point are identified. The spin images of the reference point and the four relative points are concatenated to provide additional local surface information of the reference point. A random forest model is trained with the local surface features and thereafter, used to classify 3D surface point as relief or non-relief. To effectively distinguish relief from the degraded surface region containing relief-like noise, the model is trained using three-class labels consisting of relief, background, and degraded surface region. The initial three-class result obtained from the model is refined using the k-nearest neighbors algorithm, and finally, the degraded region is re-labeled to background region. Experimental results show that the proposed method performed better than the state-of-the-art, SVM-based method with a margin of 0.68%, 3.53%, 2.25%, and 2.36%, in accuracy, precision, F1 score, and SIRI, respectively. When compared with the height- and curvature-based methods, the proposed method outperforms these existing methods with an accuracy, precision, F1 score, and SIRI of over 4%, 20%, 11%, and 12%, respectively.

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Deep Learning-Based Engraving Segmentation of 3-D Inscriptions Extracted From the Rough Surface of Ancient Stelae

et al. 2021

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Relief Extraction from Stone Monuments

Cited by 3 publications

References 0 publications

Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

A Machine Learning Method Based on 3D Local Surface Representation for Recognizing the Inscriptions on Ancient Stele

Deep Learning-Based Engraving Segmentation of 3-D Inscriptions Extracted From the Rough Surface of Ancient Stelae

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