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

Choi, Ye-Chan; Murtala, Sheriff; Jeong, Beom-Chae; Choi, Kang-Sun

doi:10.1109/access.2021.3127229

Cited by 7 publications

(2 citation statements)

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“…The research provides insights into the diverse sculptural traditions and their evolution over time. In [19] employs fractal geometry to quantitatively analyze the styles of ancient Chinese stone carving decoration. It measures the fractal dimension of the carvings and explores the relationship between fractal features and artistic styles.…”

Section: Related Workmentioning

confidence: 99%

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

Tian

2023

IJRITCC

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In recent years, advancements in data analysis techniques and deep learning algorithms have revolutionized the field of art and cultural studies. Ancient Chinese stone carving decoration holds significant historical and cultural value, reflecting the artistic and stylistic evolution of different periods. This paper explored the Weighted Long Short-Term Memory Deep Learning (WLSTM – DL) evolution and stylistic characteristics of ancient Chinese stone carving decoration through the application of image visualization techniques combined with a Long Short-Term Memory (LSTM) time-series deep learning architecture. The WLSTM-DL model uses the optimized feature selection with the grasshopper optimization for the feature extraction and selection. By analyzing a comprehensive dataset of stone carving images from different periods, the WLSTM-DL model captures the temporal relationships and patterns in the evolution of stone carving decoration. The model utilizes LSTM, a specialized deep-learning architecture for time-series data, to uncover stylistic characteristics and identify significant changes over time. The findings of this study provide valuable insights into the evolution and stylistic development of ancient Chinese stone carving decoration. The application of image visualization techniques and the WLSTM-DL model showcase the potential of data analysis and deep learning in uncovering hidden narratives and understanding the intricate details of ancient artworks.

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Section: Related Workmentioning

confidence: 99%

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

Tian

2023

IJRITCC

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“…Choi et al [34] introduced a semantic segmentation method, utilizing FC-DenseNet to extract 3-D scripts from rough surfaces, with training based on feature images from local shape features. Similarly, generalized 3-D semantic segmentation and classification networks are found in the literature, with GNN-based approaches being well-suited for holistic 3-D surface tasks [5], [6], [13].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Dual Transformer Learning for 3-D Textured Surface Segmentation

Ganapathi,

Dharejo,

Javed

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Analysis of the 3-D texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knit fabrics, and biological tissues. A 3-D texture represents a locally repeated surface variation (SV) that is independent of the overall shape of the surface and can be determined using the local neighborhood and its characteristics. Existing methods mostly employ computer vision techniques that analyze a 3-D mesh globally, derive features, and then utilize them for classification or retrieval tasks. While several traditional and learning-based methods have been proposed in the literature, only a few have addressed 3-D texture analysis, and none have considered unsupervised schemes so far. This article proposes an original framework for the unsupervised segmentation of 3-D texture on the mesh manifold. The problem is approached as a binary surface segmentation task, where the mesh surface is partitioned into textured and nontextured regions without prior annotation. The proposed method comprises a mutual transformer-based system consisting of a label generator (LG) and a label cleaner (LC). Both models take geometric image representations of the surface mesh facets and label them as texture or nontexture using an iterative mutual learning scheme. Extensive experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and state-of-the-art unsupervised techniques and performs reasonably well compared to supervised methods.

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