Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Optimization of Fresco Assembly for Accuracy. / Shchegoleva, N.; Gladkaya, M.; Dik, G.
Computational Science and Its Applications – ICCSA 2025 Workshops. Springer Nature, 2026. p. 294-308 (Lecture Notes in Computer Science; Vol. 15894 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
}
TY - GEN
T1 - Optimization of Fresco Assembly for Accuracy
AU - Shchegoleva, N.
AU - Gladkaya, M.
AU - Dik, G.
N1 - Export Date: 29 March 2026; Cited By: 0; Correspondence Address: N. Shchegoleva; St. Petersburg University, St. Petersburg, Russian Federation; email: n.shchegoleva@spbu.ru; Conference name: Workshops of the International Conference on Computational Science and Its Applications, ICCSA 2025; Conference date: 30 June 2025 through 3 July 2025; Conference code: 335039
PY - 2026
Y1 - 2026
N2 - This study introduces a novel approach to the puzzle assembly problem, leveraging textural features and geometric constraints. The texture in regions extending beyond the boundaries of puzzle pieces is estimated using inpainting and texture synthesis techniques. Feature descriptors are extracted from both the original and the synthesized images. An affinity metric is defined to quantify the correspondence between puzzle pieces, and the assembly process is formulated as an optimization problem aimed at maximizing the overall affinity score. To accelerate the alignment procedure, an image registration technique based on the Fast Fourier Transform (FFT) is employed. Experiments were conducted using different image features to study the impact of their use on assembly quality. Experimental results are presented on real and artificial data sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
AB - This study introduces a novel approach to the puzzle assembly problem, leveraging textural features and geometric constraints. The texture in regions extending beyond the boundaries of puzzle pieces is estimated using inpainting and texture synthesis techniques. Feature descriptors are extracted from both the original and the synthesized images. An affinity metric is defined to quantify the correspondence between puzzle pieces, and the assembly process is formulated as an optimization problem aimed at maximizing the overall affinity score. To accelerate the alignment procedure, an image registration technique based on the Fast Fourier Transform (FFT) is employed. Experiments were conducted using different image features to study the impact of their use on assembly quality. Experimental results are presented on real and artificial data sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
KW - Archeological reconstruction
KW - Partial matching
KW - Puzzle solving
KW - Assembly
KW - Image registration
KW - Optimization
KW - Textures
KW - Archaeological reconstruction
KW - Assembly problems
KW - Geometric constraint
KW - Inpainting
KW - Optimisations
KW - Synthesis techniques
KW - Textural feature
KW - Texture synthesis
KW - Fast Fourier transforms
UR - https://www.mendeley.com/catalogue/db9d7e0c-686e-3a83-b0f8-7e2b2a3ec76a/
U2 - 10.1007/978-3-031-97648-3_20
DO - 10.1007/978-3-031-97648-3_20
M3 - статья в сборнике материалов конференции
SN - 9783031976476
T3 - Lecture Notes in Computer Science
SP - 294
EP - 308
BT - Computational Science and Its Applications – ICCSA 2025 Workshops
PB - Springer Nature
Y2 - 30 June 2025 through 3 July 2025
ER -
ID: 151444427