Superpixel segmentation and pigment identification of colored relics based on visible spectral image

Li, Junfeng; Wan, Xiaoxia

doi:10.1016/j.saa.2017.08.042

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Super-resolution algorithms perform a posteriori processing of the spectral reflectance information obtained from HSI [2,19], and the information obtained in this way allows for segmentation and subsequent mapping of the different pigments used in the artwork. However, these algorithms are not able to detect small amounts of pigment in paintings or sculptures.…”

Section: Discussionmentioning

confidence: 99%

“…In the field of cultural heritage restoration and conservation, it is necessary to obtain HSI with high spatial resolution to perform the segmentation of large artworks. Superresolution algorithms are widely used to perform pigment segmentation of artworks [2,19]. The aim of super-resolution segmentation is to obtain images with high-spatial-resolution information from images with a low spatial resolution, based on a prior grouping of pixels with the same spectral characteristics [5,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Superresolution algorithms are widely used to perform pigment segmentation of artworks [2,19]. The aim of super-resolution segmentation is to obtain images with high-spatial-resolution information from images with a low spatial resolution, based on a prior grouping of pixels with the same spectral characteristics [5,19,20].…”

Section: Introductionmentioning

confidence: 99%

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High-Dynamic-Range Spectral Reflectance for the Segmentation of Paint Pigment: Application to Dalí’s Oil Painting Dos Figuras (1926)

Fernández-Balbuena

Manzanares

Antón

et al. 2023

Sensors

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Restorers and curators in museums sometimes find it difficult to accurately segment areas of paintings that have been contaminated with other pigments or areas that need to be restored, and work on the painting needs to be carried out with minimum possible damage. It is therefore necessary to develop measurement systems and methods that facilitate this task in the least invasive way possible. The aim of this study was to obtain high-dynamic-range (HDR) spectral reflectance and spatial resolution for Dalí’s painting entitled Two Figures (1926) in order to segment a small area of black and white pigment that was affected by the contact transfer of reddish pigment from another painting. Using Hypermatrixcam to measure the HDR spectral reflectance developed by this research team, an HDR multispectral cube of 12 images was obtained for the band 470–690 nm in steps of 20 nm. With the values obtained for the spectral reflectance of the HDR cube, the colour of the area of paint affected by the transfer was studied by calculating the a*b* components with the CIELab system. These a*b* values were then used to define two methods of segmenting the exact areas in which there was a transfer of reddish pigment. The area studied in the painting was originally black, and the contamination with reddish pigment occupied 13.87% to 32% of the total area depending on the selected method. These different solutions can be explained because the lower limit is segmentation based on pure pigment and the upper limit considers red as an exclusion of non-black pigment. Over- and under-segmentation is a common problem described in the literature related to pigment selection. In this application case, as red pigment is not original and should be removed, curators will choose the method that selects the highest red area.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Dynamic-Range Spectral Reflectance for the Segmentation of Paint Pigment: Application to Dalí’s Oil Painting Dos Figuras (1926)

Fernández-Balbuena

Manzanares

Antón

et al. 2023

Sensors

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“…Currently, the segmentation methods of visible spectral images can be divided into methods based on a single pixel [29][30][31][32], methods based on spectral feature similarity between the pixels [33][34][35], and methods based on superpixels [36]. Methods based on a single pixel consider each individual pixel as the fundamental segmentation unit to characterize the spatial distribution of pigments in colored relics.…”

Section: Adaptive Superpixel Segmentationmentioning

confidence: 99%

Adaptive Superpixel Segmentation and Pigment Identification of Colored Relics Based on Visible Spectral Image

Liu,

Wei,

et al. 2024

Preprint

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This work explores the extraction of spatial distribution and chemical composition information of pigments used in colored relics through visible spectral images of the relics. An adaptive superpixel segmentation method is proposed first to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on image homogeneity. These seed points are used as initial cluster centers in an extended SLIC algorithm designed for visible spectral images, creating superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area within the colored relics, and a pigment identification method based on visible spectral reflectance is proposed to identify the pigments used in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. The geometric features, characterized by the linear combination of normalized visible spectral reflectance and its slope and curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using Euclidean distance to determine the pigments used in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

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“…The authors also mention a variant of SLIC, called SLIC0 which adaptively changes the compactness factor. In [10] SLIC was extended for HSI by modifying the colour metric. Results show that fragmentation of individual pixels is absent.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Image Segmentation For Paint Analysis

Magro

Bonnici

Cristina

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Hyperspectral imaging (HSI) is used in analysis of paintings to obtain features hidden to the human eye by selecting specific wavelengths. Superpixel segmentation can be applied to HSI for feature extraction. A superpixel algorithm processes an image in a way in which the result includes an unnecessary amount of over-segmentation. In this work, we use over-segmentation and propose Spectral Similarity Merging (SSM), a region growing algorithm based on homogeneous spectral properties with the aim to reduce over-segmentation without compromising under-segmentation. The algorithm focuses on the similarity of the spectral shapes rather than intensity. Results show an average of 45% reduction in oversegmentation and an average of 53% improvement on the Fscore on existing superpixel segmentation algorithms.

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Superpixel segmentation and pigment identification of colored relics based on visible spectral image

Cited by 9 publications

References 26 publications

High-Dynamic-Range Spectral Reflectance for the Segmentation of Paint Pigment: Application to Dalí’s Oil Painting Dos Figuras (1926)

High-Dynamic-Range Spectral Reflectance for the Segmentation of Paint Pigment: Application to Dalí’s Oil Painting Dos Figuras (1926)

Adaptive Superpixel Segmentation and Pigment Identification of Colored Relics Based on Visible Spectral Image

Hyperspectral Image Segmentation For Paint Analysis

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