2024
Journal article
Open Access
Training a shallow NN to erase ink seepage in historical manuscripts based on a degradation model
Savino P., Tonazzini A.
In historical recto-verso manuscripts, very often the text written on the opposite page of the folio penetrates through the fiber of the paper, so that the texts on the two sides appear mixed. This is a very impairing damage that cannot be physically removed, and hinders both the work of philologists and palaeographers and the automatic analysis of linguistic contents. A procedure based on neural networks (NN) is proposed here to clean up the complex background of the manuscripts from this interference. We adopt a very simple shallow NN whose learning phase employs a training set generated from the data itself using a theoretical blending model that takes into account ink diffusion and saturation. By virtue of the parametric nature of the model, various levels of damage can be simulated in the training set, favoring a generalization capability of the NN. More explicitly, the network can be trained without the need for a large class of other similar manuscripts, but is still able, at least to some extent, to classify manuscripts with varying degrees of corruption. We compare the performance of this NN and other methods both qualitatively and quantitatively on a reference dataset and heavily damaged historical manuscripts.Source: Neural computing & applications (Print) (2024). doi:10.1007/s00521-023-09354-7
DOI: 10.1007/s00521-023-09354-7
Metrics:
Savino P., Tonazzini A.
In historical recto-verso manuscripts, very often the text written on the opposite page of the folio penetrates through the fiber of the paper, so that the texts on the two sides appear mixed. This is a very impairing damage that cannot be physically removed, and hinders both the work of philologists and palaeographers and the automatic analysis of linguistic contents. A procedure based on neural networks (NN) is proposed here to clean up the complex background of the manuscripts from this interference. We adopt a very simple shallow NN whose learning phase employs a training set generated from the data itself using a theoretical blending model that takes into account ink diffusion and saturation. By virtue of the parametric nature of the model, various levels of damage can be simulated in the training set, favoring a generalization capability of the NN. More explicitly, the network can be trained without the need for a large class of other similar manuscripts, but is still able, at least to some extent, to classify manuscripts with varying degrees of corruption. We compare the performance of this NN and other methods both qualitatively and quantitatively on a reference dataset and heavily damaged historical manuscripts.Source: Neural computing & applications (Print) (2024). doi:10.1007/s00521-023-09354-7
DOI: 10.1007/s00521-023-09354-7
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2023
Journal article
Open Access
Restoration and content analysis of ancient manuscripts via color space based segmentation
Hanif M., Tonazzini A., Hussain S. F., Khalil A., Habib U.
Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.Source: PloS one 18 (2023). doi:10.1371/journal.pone.0282142
DOI: 10.1371/journal.pone.0282142
Metrics:
Hanif M., Tonazzini A., Hussain S. F., Khalil A., Habib U.
Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.Source: PloS one 18 (2023). doi:10.1371/journal.pone.0282142
DOI: 10.1371/journal.pone.0282142
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2023
Conference article
Open Access
Mathematical models and neural networks for the description and the correction of typical distortions of historical manuscripts
Savino P., Tonazzini A.
Historical manuscripts are very often degraded by the seeping or transparency of the ink from the page opposite side. Suppressing the interfering text can be of great aid to philologists and paleographers who aim at interpreting the primary text, and nowadays also for the automatic analysis of the text. We formerly proposed a data model, which approximately describes this damage, to generate an artificial training set able to teach a shallow neural network how to classify pixels in clean or corrupted. This NN has proved to be effective in classifying manuscripts where the degradation can be also widely variable. In this paper, we modify the architecture of the NN to better account for ink saturation in text overlay areas, by including a specific class for these pixels. From the experiments, the improvement of the classification and then the restoration is significant.Source: ICCSA 2023 Workshops, pp. 545–557, Athens, Greece, 3-6/07/2023
DOI: 10.1007/978-3-031-37117-2_37
Metrics:
Savino P., Tonazzini A.
Historical manuscripts are very often degraded by the seeping or transparency of the ink from the page opposite side. Suppressing the interfering text can be of great aid to philologists and paleographers who aim at interpreting the primary text, and nowadays also for the automatic analysis of the text. We formerly proposed a data model, which approximately describes this damage, to generate an artificial training set able to teach a shallow neural network how to classify pixels in clean or corrupted. This NN has proved to be effective in classifying manuscripts where the degradation can be also widely variable. In this paper, we modify the architecture of the NN to better account for ink saturation in text overlay areas, by including a specific class for these pixels. From the experiments, the improvement of the classification and then the restoration is significant.Source: ICCSA 2023 Workshops, pp. 545–557, Athens, Greece, 3-6/07/2023
DOI: 10.1007/978-3-031-37117-2_37
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2023
Conference article
Unknown
Preprocessing of recto-verso printed documents based on neural networks for text analysis
Savino P., Tonazzini A.
Among the many and varied damages affecting ancient documents, the penetration of ink from one side of the page to the other is one of the most frequent and invasive. In this work, we are interested in binarizing such degraded documents, for the application of OCR or other automatic text analysis tools, which can help philologists and palaeographers in text transcription. We previously proposed a data model that roughly describes this damage for front-to-back documents, and used it to generate an artificial training set that can teach a shallow neural network how to classify pixels on both sides into clean or corrupt. We show that this joint processing of the two sides of the document can significantly improve binarization and therefore OCR and other text analysis tasks, compared to the separate processing of the single sides, using the same information.Source: 3rd Conference on Digital Preservation and processing technology of Written Heritage, in conjunction with the 7th IEEE International Congress on Information Science and Technology (IEEE CiSt'23), Agadir - Essaouira, Morocco, 16-22/12/2023
Savino P., Tonazzini A.
Among the many and varied damages affecting ancient documents, the penetration of ink from one side of the page to the other is one of the most frequent and invasive. In this work, we are interested in binarizing such degraded documents, for the application of OCR or other automatic text analysis tools, which can help philologists and palaeographers in text transcription. We previously proposed a data model that roughly describes this damage for front-to-back documents, and used it to generate an artificial training set that can teach a shallow neural network how to classify pixels on both sides into clean or corrupt. We show that this joint processing of the two sides of the document can significantly improve binarization and therefore OCR and other text analysis tasks, compared to the separate processing of the single sides, using the same information.Source: 3rd Conference on Digital Preservation and processing technology of Written Heritage, in conjunction with the 7th IEEE International Congress on Information Science and Technology (IEEE CiSt'23), Agadir - Essaouira, Morocco, 16-22/12/2023
See at: CNR ExploRA
2022
Journal article
Open Access
Blind bleed-through removal in color ancient manuscripts
Hanif M., Tonazzini A., Hussain S. F., Habib U., Salerno E., Savino P., Halim Z.
Archaic manuscripts are an important part of ancient civilization. Unfortunately, such documents are often affected by various age related degradations, which impinge their legibility and information contents, and destroy their original look. In general, these documents are composed of three layers of information: foreground text, background, and unwanted degradation in the form of patterns interfering with the main text. In this work, we are presenting a color space based image segmentation technique to separate and remove the bleed-through degradation in digital ancient manuscripts. The main theme is to improve their readability and restore their original aesthetic look. For each pixel, a feature vector is created using color spectral and spatial location information. A pixel based segmentation method using Gaussian Mixture Model (GMM) is employed, assuming that each feature vector corresponds to a Gaussian distribution. Based on this assumption, each pixel is supposed to be drawn from a mixture of Gaussian distribution, with unknown parameters. The Expectation-Maximization (EM) approach is then used to estimate the unknown GMM parameters. The appropriate class label for each pixel is then estimated using posterior probability and GMM parameters. Unlike other binarization based document restoration method where the focus is on text extraction, we are more interested in restoring the aesthetically pleasing look of the ancient documents.The experimental results validate the usefulness of proposed method in terms of successful bleed-through identification and removal, while preserving foreground-text and background information.Source: Multimedia tools and applications (Dordrecht. Online) 82 (2022): 12321–12335. doi:10.1007/s11042-022-13755-6
DOI: 10.1007/s11042-022-13755-6
Metrics:
Hanif M., Tonazzini A., Hussain S. F., Habib U., Salerno E., Savino P., Halim Z.
Archaic manuscripts are an important part of ancient civilization. Unfortunately, such documents are often affected by various age related degradations, which impinge their legibility and information contents, and destroy their original look. In general, these documents are composed of three layers of information: foreground text, background, and unwanted degradation in the form of patterns interfering with the main text. In this work, we are presenting a color space based image segmentation technique to separate and remove the bleed-through degradation in digital ancient manuscripts. The main theme is to improve their readability and restore their original aesthetic look. For each pixel, a feature vector is created using color spectral and spatial location information. A pixel based segmentation method using Gaussian Mixture Model (GMM) is employed, assuming that each feature vector corresponds to a Gaussian distribution. Based on this assumption, each pixel is supposed to be drawn from a mixture of Gaussian distribution, with unknown parameters. The Expectation-Maximization (EM) approach is then used to estimate the unknown GMM parameters. The appropriate class label for each pixel is then estimated using posterior probability and GMM parameters. Unlike other binarization based document restoration method where the focus is on text extraction, we are more interested in restoring the aesthetically pleasing look of the ancient documents.The experimental results validate the usefulness of proposed method in terms of successful bleed-through identification and removal, while preserving foreground-text and background information.Source: Multimedia tools and applications (Dordrecht. Online) 82 (2022): 12321–12335. doi:10.1007/s11042-022-13755-6
DOI: 10.1007/s11042-022-13755-6
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2022
Contribution to book
Open Access
Blind source separation in laser-induced breakdown spectroscopy
Tonazzini A., Salerno E., Pagnotta S.
Many years have passed since the birth of laser induced breakdown analysis and several steps forward have been made for the improvement of the technique from a hardware and software point of view. Libs has been skyrocketed, literally. Now, the need to automate the process of recognition, classification and quantification of the analytes becomes more and more pressing. In the chapters of this book, the new advances regarding these issues have been described. Here, an attempt to separate the spectra of the analytes will be described, which uses some of the most common blind source separation techniques. This type of approach is not a usual practice in Libs, so our contribution wants to provide a taste of the potential of this method for anyone who wants to try their hand at analyzing real data.Source: Chemometrics and Numerical Methods in LIBS, edited by Vincenzo Palleschi, pp. 189–212. New York: J. Wiley & sons, 2022
DOI: 10.1002/9781119759614.ch8
Metrics:
Tonazzini A., Salerno E., Pagnotta S.
Many years have passed since the birth of laser induced breakdown analysis and several steps forward have been made for the improvement of the technique from a hardware and software point of view. Libs has been skyrocketed, literally. Now, the need to automate the process of recognition, classification and quantification of the analytes becomes more and more pressing. In the chapters of this book, the new advances regarding these issues have been described. Here, an attempt to separate the spectra of the analytes will be described, which uses some of the most common blind source separation techniques. This type of approach is not a usual practice in Libs, so our contribution wants to provide a taste of the potential of this method for anyone who wants to try their hand at analyzing real data.Source: Chemometrics and Numerical Methods in LIBS, edited by Vincenzo Palleschi, pp. 189–212. New York: J. Wiley & sons, 2022
DOI: 10.1002/9781119759614.ch8
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onlinelibrary.wiley.com | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A shallow neural net with model-based learning for the virtual restoration of recto-verso manuscript
Savino P., Tonazzini A.
We propose a fast procedure based on neural networks (NN) to correct the typically complex background of recto-verso historical manuscripts, where the texts of the two sides often appear mixed. The purpose is to eliminate the interfering, shining-through text, to facilitate both the work of philologists and paleographers and the automatic analysis of the linguistic contents. We adapt the learning phase of a very simple shallow NN to exploit the information of the registered recto and verso sides of the manuscript without the need for a large class of other similar manuscripts. Hence, the training set is self-generated from the data images based on a theoretical mixing model that accounts for ink spreading through the paper fiber and for ink saturation in the text superposition areas. Operationally, we select pairs of patches containing clean text from the manuscript and then mix them symmetrically using the model with varying parameters that span the allowed range. This makes the NN able to generalize to diverse amounts of ink seeping and then classify different manuscripts. We show comparisons between the results obtained on heavily damaged manuscripts with this NN and other approaches. From a qualitative point of view, the proposed method seems quite promising.Source: VIPERC2022: 1st International Virtual Conference on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, Online event, 12/09/2022
Savino P., Tonazzini A.
We propose a fast procedure based on neural networks (NN) to correct the typically complex background of recto-verso historical manuscripts, where the texts of the two sides often appear mixed. The purpose is to eliminate the interfering, shining-through text, to facilitate both the work of philologists and paleographers and the automatic analysis of the linguistic contents. We adapt the learning phase of a very simple shallow NN to exploit the information of the registered recto and verso sides of the manuscript without the need for a large class of other similar manuscripts. Hence, the training set is self-generated from the data images based on a theoretical mixing model that accounts for ink spreading through the paper fiber and for ink saturation in the text superposition areas. Operationally, we select pairs of patches containing clean text from the manuscript and then mix them symmetrically using the model with varying parameters that span the allowed range. This makes the NN able to generalize to diverse amounts of ink seeping and then classify different manuscripts. We show comparisons between the results obtained on heavily damaged manuscripts with this NN and other approaches. From a qualitative point of view, the proposed method seems quite promising.Source: VIPERC2022: 1st International Virtual Conference on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, Online event, 12/09/2022
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ceur-ws.org | ISTI Repository | ISTI Repository | CNR ExploRA
2022
Report
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
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2021
Journal article
Open Access
Analysis of diagnostic images of artworks and feature extraction: design of a methodology
Amura A., Aldini A., Pagnotta S., Salerno E., Tonazzini A., Triolo P.
Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7030053
DOI: 10.3390/jimaging7030053
Metrics:
Amura A., Aldini A., Pagnotta S., Salerno E., Tonazzini A., Triolo P.
Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7030053
DOI: 10.3390/jimaging7030053
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ISTI Repository | DOAJ-Articles | Journal of Imaging | CNR ExploRA
2021
Journal article
Open Access
Integration of multiple resolution data in 3D chromatin reconstruction using ChromStruct
Caudai C., Zoppè M., Tonazzini A., Merelli I., Salerno E.
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct's 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.Source: Biology (Basel) 10 (2021): 338. doi:10.3390/biology10040338
DOI: 10.3390/biology10040338
Metrics:
Caudai C., Zoppè M., Tonazzini A., Merelli I., Salerno E.
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct's 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.Source: Biology (Basel) 10 (2021): 338. doi:10.3390/biology10040338
DOI: 10.3390/biology10040338
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Europe PubMed Central | ISTI Repository | www.mdpi.com | Biology | CNR ExploRA
2021
Report
Open Access
SI-Lab Annual Research Report 2020
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
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ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Challenges in the digital analysis of historical laminated manuscripts
Del Grosso A. M., Fihri D. F., Mohajir M. El, Tonazzini A., Nahli O.
In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. The most significant degradation caused by lamination is that the parchment or paper support loses its flatness, and usually presents ripples and warnings. This, together with the affixed translucent varnish, dramatically impair the digital acquisition process, so that light reflections in the more disparate directions affect the digital images. A digital system to contrast this irreversible and progressive degradation and to enable an effective access to the fragile asset should provide a number of functionalities: specialized digitization, able to avoid reflections as much as possible; image enhancement, devised to correct the residual degradations and enhance the text for an easier legibility; semi-automatic transcription of the virtually restored pages; and, finally, scholarly encoding and linguistic analysis, which should adapt existing tools to the specificity of the primary source (writing system and language). As a case study, we will make reference to the "Poem in Rajaz on medicine", written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. The feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the "Poem in Rajaz on medicine" could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.Source: International Journal of Information Science and Technology 5 (2021): 34–43. doi:10.57675/IMIST.PRSM/ijist-v5i1.190
DOI: 10.57675/imist.prsm/ijist-v5i1.190
Metrics:
Del Grosso A. M., Fihri D. F., Mohajir M. El, Tonazzini A., Nahli O.
In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. The most significant degradation caused by lamination is that the parchment or paper support loses its flatness, and usually presents ripples and warnings. This, together with the affixed translucent varnish, dramatically impair the digital acquisition process, so that light reflections in the more disparate directions affect the digital images. A digital system to contrast this irreversible and progressive degradation and to enable an effective access to the fragile asset should provide a number of functionalities: specialized digitization, able to avoid reflections as much as possible; image enhancement, devised to correct the residual degradations and enhance the text for an easier legibility; semi-automatic transcription of the virtually restored pages; and, finally, scholarly encoding and linguistic analysis, which should adapt existing tools to the specificity of the primary source (writing system and language). As a case study, we will make reference to the "Poem in Rajaz on medicine", written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. The feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the "Poem in Rajaz on medicine" could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.Source: International Journal of Information Science and Technology 5 (2021): 34–43. doi:10.57675/IMIST.PRSM/ijist-v5i1.190
DOI: 10.57675/imist.prsm/ijist-v5i1.190
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innove.org | ISTI Repository | International Journal of Information Science and Technology | doi.org | CNR ExploRA
2021
Journal article
Open Access
A procedure for the correction of back-to-front degradations in archival manuscripts with preservation of the original appearance
Savino P., Tonazzini A.
Virtual restoration of digital copies of the human documental heritage is crucial for facilitating both the traditional work of philologists and paleographers and the automatic analysis of the contents. Here we propose a practical and fast procedure for the correction of the typically complex background of recto-verso historical manuscripts. The procedure has two main, distinctive features: it does not need for a preliminary registration of the two page sides, and it is non-invasive, as it does not alter the original appearance of the manuscript. This makes it suitable for the routinary use in the archives, and permits an easier fruition of the manuscripts, without any information being lost. In the ¯rst stage, the detection of both the primary text and the spurious strokes is performed via soft segmentation, based on the statistical decorrelation of the two recto and verso images. In the second stage, the noisy pattern is substituted with pixels that simulate the texture of the clean surrounding background, through an e±cient technique of image inpainting. As shown in the experimental results, evaluated both qualitatively and quantitatively, the proposed procedure is able to perform a ¯ne and selective removal of the degradation, while preserving other informative marks of the manuscript history.Source: Vietnam journal of computer science (Online) (2021). doi:10.1142/S2196888822500099
DOI: 10.1142/s2196888822500099
Metrics:
Savino P., Tonazzini A.
Virtual restoration of digital copies of the human documental heritage is crucial for facilitating both the traditional work of philologists and paleographers and the automatic analysis of the contents. Here we propose a practical and fast procedure for the correction of the typically complex background of recto-verso historical manuscripts. The procedure has two main, distinctive features: it does not need for a preliminary registration of the two page sides, and it is non-invasive, as it does not alter the original appearance of the manuscript. This makes it suitable for the routinary use in the archives, and permits an easier fruition of the manuscripts, without any information being lost. In the ¯rst stage, the detection of both the primary text and the spurious strokes is performed via soft segmentation, based on the statistical decorrelation of the two recto and verso images. In the second stage, the noisy pattern is substituted with pixels that simulate the texture of the clean surrounding background, through an e±cient technique of image inpainting. As shown in the experimental results, evaluated both qualitatively and quantitatively, the proposed procedure is able to perform a ¯ne and selective removal of the degradation, while preserving other informative marks of the manuscript history.Source: Vietnam journal of computer science (Online) (2021). doi:10.1142/S2196888822500099
DOI: 10.1142/s2196888822500099
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ISTI Repository | ISTI Repository | www.worldscientific.com | Vietnam Journal of Computer Science | CNR ExploRA
2021
Journal article
Open Access
Algoritmi di Image Analysis applicati alle immagini diagnostiche: nuove metodologie per l'analisi conoscitiva ed estrazione semi-automatica della mappatura del degrado
Amura A., Aldini A., Landi L., Pisani L., Salerno E., Soro M. V., Tonazzini A., Torre M., Triolo Paolo A. M., Zantedeschi G.
Questo lavoro propone una metodologia di analisi statistica delle immagini diagnostiche finalizzata a migliorarne la lettura e a facilitare la trascrizione grafica dello stato di conservazione di beni artistici, rendendola puntuale e ripetibile. Si presenta come caso di studio un piccolo dipinto ad olio su tela di autore ignoto in cattivo stato di conservazione. Utilizzando il metodo citato, basato su un approccio semi-automatico di estrazione delle aree di interesse, si otterranno delle schede di rilievo relative allo stato di conservazione con le quali sarà possibile eseguire statistiche zonali per calcolare la percentuale dell'area danneggiata rispetto all'intera superficie del dipinto. Le operazioni mostrate possono essere applicate ad ogni tipologia di immagine diagnostica, studiando in maniera pi? oggettiva lo stato di conservazione di qualsivoglia manufatto.Source: Kermes (Firenze) Anno XXXIV (2021): 17–24.
Amura A., Aldini A., Landi L., Pisani L., Salerno E., Soro M. V., Tonazzini A., Torre M., Triolo Paolo A. M., Zantedeschi G.
Questo lavoro propone una metodologia di analisi statistica delle immagini diagnostiche finalizzata a migliorarne la lettura e a facilitare la trascrizione grafica dello stato di conservazione di beni artistici, rendendola puntuale e ripetibile. Si presenta come caso di studio un piccolo dipinto ad olio su tela di autore ignoto in cattivo stato di conservazione. Utilizzando il metodo citato, basato su un approccio semi-automatico di estrazione delle aree di interesse, si otterranno delle schede di rilievo relative allo stato di conservazione con le quali sarà possibile eseguire statistiche zonali per calcolare la percentuale dell'area danneggiata rispetto all'intera superficie del dipinto. Le operazioni mostrate possono essere applicate ad ogni tipologia di immagine diagnostica, studiando in maniera pi? oggettiva lo stato di conservazione di qualsivoglia manufatto.Source: Kermes (Firenze) Anno XXXIV (2021): 17–24.
See at:
ISTI Repository | www.kermes-restauro.it | CNR ExploRA
2020
Journal article
Open Access
Color segmentation and neural networks for automatic graphic relief of the state of conservation of artworks
Amura A., Tonazzini A., Salerno E., Pagnotta S., Palleschi V.
This paper proposes a semi-automated methodology based on a sequence of analysis processes performed on multispectral images of artworks and aimed at the extraction of vector maps regarding their state of conservation. The graphic relief of the artwork represents the main instrument of communication and synthesis of information and data acquired on cultural heritage during restoration. Despite the widespread use of informatics tools, currently, these operations are still extremely subjective and require high execution times and costs. In some cases, manual execution is particularly complicated and almost impossible to carry out. The methodology proposed here allows supervised, partial automation of these procedures avoids approximations and drastically reduces the work times, as it makes a vector drawing by extracting the areas directly from the raster images. We propose a procedure for color segmentation based on principal/independent component analysis (PCA/ICA) and SOM neural networks and, as a case study, present the results obtained on a set of multispectral reproductions of a painting on canvas.Source: Cultura e scienza del colore 12 (2020): 7–15. doi:10.23738/CCSJ.120201
DOI: 10.23738/ccsj.120201
Metrics:
Amura A., Tonazzini A., Salerno E., Pagnotta S., Palleschi V.
This paper proposes a semi-automated methodology based on a sequence of analysis processes performed on multispectral images of artworks and aimed at the extraction of vector maps regarding their state of conservation. The graphic relief of the artwork represents the main instrument of communication and synthesis of information and data acquired on cultural heritage during restoration. Despite the widespread use of informatics tools, currently, these operations are still extremely subjective and require high execution times and costs. In some cases, manual execution is particularly complicated and almost impossible to carry out. The methodology proposed here allows supervised, partial automation of these procedures avoids approximations and drastically reduces the work times, as it makes a vector drawing by extracting the areas directly from the raster images. We propose a procedure for color segmentation based on principal/independent component analysis (PCA/ICA) and SOM neural networks and, as a case study, present the results obtained on a set of multispectral reproductions of a painting on canvas.Source: Cultura e scienza del colore 12 (2020): 7–15. doi:10.23738/CCSJ.120201
DOI: 10.23738/ccsj.120201
Metrics:
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jcolore.gruppodelcolore.it | ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
Digital safeguard of laminated historical manuscripts: the treatise "Poem in Rajaz on medicine" as a case study
Del Grosso A. M., Fassi Fihri D., El Mohajir M., Nahli O., Tonazzini A.
In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. As a case study, we will make reference to the "Poem in Rajaz on medicine", written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. The conceived system should have at least four main functionalities: image acquisition (i.e. digitization), image enhancement, text encoding, and linguistic analysis. Based on the evaluation of the manuscript damages, the acquisition set up should be designed in such a way to be able to avoid reflections as much as possible. Suitable digital image processing techniques should also be devised to correct the residual degradations and enhance the text for an easier legibility. Finally, semi-automatic transcription, scholarly encoding and linguistic analysis, to be performed on the virtually restored pages, should adapt existing tools to the specificity of the primary source writing system and language. The feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the "Poem in Rajaz on medicine" could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.Source: CiSt'2020 - 6th IEEE Congress on Information Science & Technology, pp. 192–197, Agadir-Essaouira, Morocco, June 5-12, 2021
DOI: 10.1109/cist49399.2021.9357192
Metrics:
Del Grosso A. M., Fassi Fihri D., El Mohajir M., Nahli O., Tonazzini A.
In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. As a case study, we will make reference to the "Poem in Rajaz on medicine", written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. The conceived system should have at least four main functionalities: image acquisition (i.e. digitization), image enhancement, text encoding, and linguistic analysis. Based on the evaluation of the manuscript damages, the acquisition set up should be designed in such a way to be able to avoid reflections as much as possible. Suitable digital image processing techniques should also be devised to correct the residual degradations and enhance the text for an easier legibility. Finally, semi-automatic transcription, scholarly encoding and linguistic analysis, to be performed on the virtually restored pages, should adapt existing tools to the specificity of the primary source writing system and language. The feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the "Poem in Rajaz on medicine" could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.Source: CiSt'2020 - 6th IEEE Congress on Information Science & Technology, pp. 192–197, Agadir-Essaouira, Morocco, June 5-12, 2021
DOI: 10.1109/cist49399.2021.9357192
Metrics:
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
Estimation of the spatial chromatin structure based on a multiresolution bead-chain model
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/tcbb.2018.2791439
Metrics:
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/tcbb.2018.2791439
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ISTI Repository | IEEE/ACM Transactions on Computational Biology and Bioinformatics | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data
Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.
A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669
DOI: 10.1109/tcbb.2018.2838669
Metrics:
Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.
A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669
DOI: 10.1109/tcbb.2018.2838669
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ISTI Repository | IEEE/ACM Transactions on Computational Biology and Bioinformatics | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
A new infrared true-color approach for visible-infrared multispectral image analysis
Grifoni E., Campanella B., Legnaioli S., Lorenzetti G., Marras L., Pagnotta S., Palleschi V., Poggialini F., Salerno E., Tonazzini A.
In this article, we present a newmethod for the analysis of visible/Infraredmultispectral sets producing chromatically faithful false-color images, whichmaintain a good readability of the information contained in the non-visible Infrared band. Examples of the application of this technique are given on the multispectral images acquired on the Pietà of Santa Croce of Agnolo Bronzino (1569, Florence) and on the analysis and visualization of the multispectral data obtained on Etruscanmural paintings (Tomb of the Monkey, Siena, Italy, V century B.C.). The fidelity of the chromatic appearance of the resulting images, coupled to the effective visualization of the information contained in the Infrared band, opens interesting perspectives for the use of the method for visualization and presentation of the results of multispectral analysis in Cultural Heritage diffusion, research, and diagnostics.Source: ACM journal on computing and cultural heritage (Print) 12 (2019): 8:1–8:11. doi:10.1145/3241065
DOI: 10.1145/3241065
Metrics:
Grifoni E., Campanella B., Legnaioli S., Lorenzetti G., Marras L., Pagnotta S., Palleschi V., Poggialini F., Salerno E., Tonazzini A.
In this article, we present a newmethod for the analysis of visible/Infraredmultispectral sets producing chromatically faithful false-color images, whichmaintain a good readability of the information contained in the non-visible Infrared band. Examples of the application of this technique are given on the multispectral images acquired on the Pietà of Santa Croce of Agnolo Bronzino (1569, Florence) and on the analysis and visualization of the multispectral data obtained on Etruscanmural paintings (Tomb of the Monkey, Siena, Italy, V century B.C.). The fidelity of the chromatic appearance of the resulting images, coupled to the effective visualization of the information contained in the Infrared band, opens interesting perspectives for the use of the method for visualization and presentation of the results of multispectral analysis in Cultural Heritage diffusion, research, and diagnostics.Source: ACM journal on computing and cultural heritage (Print) 12 (2019): 8:1–8:11. doi:10.1145/3241065
DOI: 10.1145/3241065
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ISTI Repository | dl.acm.org | Journal on Computing and Cultural Heritage | CNR ExploRA
2019
Journal article
Open Access
Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review
Tonazzini A., Salerno E., Abdel-Salam Z. A., Harith M. A., Marras L., Botto A., Campanella B., Legnaioli S., Pagnotta S., Poggialini F., Palleschi V.
In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings. (C) 2019 The Authors. Published by Elsevier B.V.Source: Journal of Advanced Research (Print) (Print) 17 (2019): 31–42. doi:10.1016/j.jare.2019.01.003
DOI: 10.1016/j.jare.2019.01.003
Metrics:
Tonazzini A., Salerno E., Abdel-Salam Z. A., Harith M. A., Marras L., Botto A., Campanella B., Legnaioli S., Pagnotta S., Poggialini F., Palleschi V.
In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings. (C) 2019 The Authors. Published by Elsevier B.V.Source: Journal of Advanced Research (Print) (Print) 17 (2019): 31–42. doi:10.1016/j.jare.2019.01.003
DOI: 10.1016/j.jare.2019.01.003
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Journal of Advanced Research | europepmc.org | Journal of Advanced Research | ISTI Repository | Journal of Advanced Research | CNR ExploRA