Multispectral image restoration of historical documents based on LAAMs and mathematical morphology

Lechuga-S., Edwin; Valdiviezo-N., Juan C.; Urcid, Gonzalo

doi:10.1117/12.2061479

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Filters are used to reduce the range of wavelengths from the light source [20], [41], [47], [48], [89], [104], [115] or to exclude illuminating light when imaging fluorescence [23], [34], [45], [52], however the make and model are rarely described. When filters are used in conjunction with a broadband light source, the spectral ranges are often stated ( [41], [47], [48], [115]), whereas when used to capture fluorescence, often only the region of the spectrum is documented [23], [34], [45].…”

Section: Filtersmentioning

confidence: 99%

“…Various image processing techniques have been used to facilitate the identification of individual features and attributes of artefacts by highlighting differences and similarities between images in the capture sequence. The approach taken depends on the user, artefact and institution, and includes: Contrast Stretching [41], [88], [94], [116], [140], Spectral Curves [37], [49], [81], [91], [98], [114], [135], [141], Principal Component Analysis [21], [38], [74], [110], [126], Independent Component Analysis [34], [89], [139], [142], Linear Discriminate Analysis [55], [143]- [146], Spectral Mixture Analysis [30], [89], [98], [104], [139], Mosaicing [35], [45], [77], [91], [100], Clustering [22], [37], [47], [104], [147], [148], Spectral Angle Mapper [55], [149]- [153] a...…”

Section: Image Processingmentioning

confidence: 99%

“…Pixels with similar properties can be grouped together using a clustering algorithm such as Kmeans clustering [44], [47], [104], [147], [148], fuzzy C-means [37], [44], [147], Gaussian mixture models [148], and the Linde, Buzo and Gray clustering algorithm [22], [170]. Since clustering methods are unsupervised techniques, they do not require labels and instead cluster the data based on some distance measure.…”

Section: Image Processingmentioning

confidence: 99%

“…Spectral Mixture Analysis separates the spectral response of a pixel that contains a mixture of substances into the individual spectral responses of the materials [171]. It is occasionally applied to MSI [30], [42], [47], [76], [98], [104], [139], [172] to separate a mixture of different materials, such as pigments or inks (such as for palimpsests). The spectral contrast between the separate materials must be large enough in order to distinguish between them: it is therefore more suited to hyperspectral than multispectral imaging due to its higher spectral resolution [172]).…”

Section: Image Processingmentioning

confidence: 99%

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Understanding multispectral imaging of cultural heritage: Determining best practice in MSI analysis of historical artefacts

Jones¹,

Duffy

Gibson³

et al. 2020

Journal of Cultural Heritage

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Although multispectral imaging (MSI) of cultural heritage, such as manuscripts, documents and artwork, is becoming more popular, a variety of approaches are taken and methods are often inconsistently documented. Furthermore, no overview of the process of MSI capture and analysis with current technology has previously been published. This research was undertaken to determine current best practice in the deployment of MSI, highlighting areas that need further research, whilst providing recommendations regarding approach and documentation. An Action Research methodology was used to characterise the current pipeline, including: literature review; unstructured interviews and discussion of results with practitioners; and reflective practice whilst undertaking MSI analysis. The pipeline and recommendations from this research will improve project management by increasing clarity of published outcomes, the reusability of data, and encouraging a more open discussion of process and application within the MSI community. The importance of thorough documentation is emphasised, which will encourage sharing of best practice and results, improving community deployment of the technique. The findings encourage efficient use and reporting of MSI, aiding access to historical analysis. We hope this research will be useful to digitisation professionals, curators and conservators, allowing them to compare and contrast current practices. Highlights:• A range of approaches are undertaken for multispectral imaging of heritage.• An Action Research methodology was used to characterise the current pipeline.• A set of recommendations for best practice and documentation are provided.

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Section: Filtersmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

See 2 more Smart Citations

Understanding multispectral imaging of cultural heritage: Determining best practice in MSI analysis of historical artefacts

Jones¹,

Duffy

Gibson³

et al. 2020

Journal of Cultural Heritage

View full text Add to dashboard Cite

show abstract

“…Além disso, como elas são baseadas em operações em reticulados, elas são computacionalmente mais baratas do que muitos modelos de memórias autoassociativas tradicionais. As AMMs originais têm sido aplicadas com sucesso em vários problemas tais como classificação de pacientes com esquizofrenia (Chyzhyk and Graña, 2015), restauração de documentos históricos (Lechuga-S. et al, 2014;Valdiviezo-N et al, 2017) e análise e processamentos de imagem hiperespectral (Graña and Chyzhyk, 2016).…”

Section: Introductionunclassified

Memórias autoassociativas de projeções em reticulados completos

Santos¹

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Nessa jornada que percorri várias pessoas e instituições foram fundamentais para alcançar este objetivo. Assim, gostaria de agradecer especialmente:Ao bom Deus por ter me guiado nesta jornada e sido minha luz nos momentos de dificuldades.Ao prof. Marcos Eduardo Valle pela incessante disposição em dirimir as dúvidas e as incertezas. Agradecer-lhe pela orientação é muito pouco e tenho a convicção que ele sabe da relevância da sua orientação na elaboração desta tese e na minha formação profissional.Aos membros da banca pelas sugestões e críticas. Tenho a plena certeza que foram essenciais para o enriquecimento deste trabalho.À minha família pelo incentivo, compreensão e apoio ao longo desta etapa da minha vida. Direciono um agradecimento especial a minha mãe, Margarida, por ser um modelo de coragem e incentivo ao longo desta caminhada.A todos os amigos do IMECC, Arlyson, Douglas, Isaac, Fidelis, Aline e, tantos outros, pela influência em meu desenvolvimento científico.Aos meus queridos amigos Welton e Karine pela amizade e inestimável apoio mesmo estando distante.À Universidade Estadual de Campinas (UNICAMP) por me acolher como aluno da pós-graduação. À Universidade Federal do Recôncavo da Bahia (UFRB) que permitiu a realização do doutoramento por meio do seu programa de capacitação docente. Estendo este agradecimento aos professores e amigos da

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Morphological Associative Memories

Valle

Sussner

Santos

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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Morphological associative memories (MAMs) belong to the class of morphological neural networks that – in contrast to classical models of artificial neural networks – employ operators of mathematical morphology in order to aggregate the synaptic weights and the incoming signal. The original and most widely known MAM model as well as some recent modifications of this model can be formulated using matrix operation of a lattice algebra called minimax algebra that is closely related to the umbra approach of grayscale mathematical morphology. Fuzzy MAM models execute basic operations of fuzzy mathematical morphology in the recall phase. In this article, we briefly review the aforementioned MAM models and discuss some applications in classification, hyperspectral image analysis, and prediction.

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Multispectral image restoration of historical documents based on LAAMs and mathematical morphology

Cited by 6 publications

References 9 publications

Understanding multispectral imaging of cultural heritage: Determining best practice in MSI analysis of historical artefacts

Understanding multispectral imaging of cultural heritage: Determining best practice in MSI analysis of historical artefacts

Memórias autoassociativas de projeções em reticulados completos

Morphological Associative Memories

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