2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) 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 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) 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 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2024
Conference article
Open Access
Preprocessing of recto-verso printed documents based on neural networks for text analysis
Savino P, Tonazzini A
Among the many and varied damages affectingancient documents, the penetration of ink from one side of thepage to the other is one of the most frequent and invasive. In thiswork, we are interested in binarizing such degraded documents,for the application of OCR or other automatic text analysistools, which can help philologists and palaeographers in texttranscription. We previously proposed a data model that roughlydescribes this damage for front-to-back documents, and used itto generate an artificial training set that can teach a shallowneural network how to classify pixels on both sides into clean orcorrupt. We show that this joint processing of the two sides of thedocument can significantly improve binarization and thereforeOCR and other text analysis tasks, compared to the separateprocessing of the single sides, using the same information.DOI: 10.1109/cist56084.2023.10409970
Metrics:
Savino P, Tonazzini A
Among the many and varied damages affectingancient documents, the penetration of ink from one side of thepage to the other is one of the most frequent and invasive. In thiswork, we are interested in binarizing such degraded documents,for the application of OCR or other automatic text analysistools, which can help philologists and palaeographers in texttranscription. We previously proposed a data model that roughlydescribes this damage for front-to-back documents, and used itto generate an artificial training set that can teach a shallowneural network how to classify pixels on both sides into clean orcorrupt. We show that this joint processing of the two sides of thedocument can significantly improve binarization and thereforeOCR and other text analysis tasks, compared to the separateprocessing of the single sides, using the same information.DOI: 10.1109/cist56084.2023.10409970
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CNR IRIS 
| ieeexplore.ieee.org | doi.org 
| IRIS Cnr | CNR IRIS | CNR IRIS
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
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
DOI: 10.1007/s00521-023-09354-7
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CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
An edge-preserving regularization model for the demosaicing of noisy color images
Boccuto A., Gerace I., Giorgetti V., Martinelli F., Tonazzini A.
This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.Source: JOURNAL OF MATHEMATICAL IMAGING AND VISION, vol. 66 (issue 5), pp. 904-925
DOI: 10.1007/s10851-024-01204-y
Metrics:
Boccuto A., Gerace I., Giorgetti V., Martinelli F., Tonazzini A.
This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.Source: JOURNAL OF MATHEMATICAL IMAGING AND VISION, vol. 66 (issue 5), pp. 904-925
DOI: 10.1007/s10851-024-01204-y
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Journal of Mathematical Imaging and Vision | IRIS Cnr | IRIS Cnr | Software Heritage | Archivio Istituzionale della Ricerca - Università degli Studi di Perugia | GitHub | Archivio Istituzionale della Ricerca - Università degli Studi di Perugia | CNR IRIS | Archivio Istituzionale della Ricerca - Università degli Studi di Perugia
2024
Journal article
Open Access
Mathematical data models and context-based features for enhancing historical degraded manuscripts using neural network classification
Savino P., Tonazzini A.
A common cause of deterioration in historic manuscripts is ink transparency or bleeding from the opposite page. Philologists and paleographers can significantly benefit from minimizing these interferences when attempting to decipher the original text. Additionally, computer-aided text analysis can also gain from such text enhancement. In previous work, we proposed the use of neural networks (NNs) in combination with a data model that characterizes the damage when both sides of a page have been digitized. This approach offers the distinct advantage of allowing the creation of an artificial training set that teaches the NN to differentiate between clean and damaged pixels. We tested this concept using a shallow NN, which proved effective in categorizing texts with varying levels of deterioration. In this study, we adapt the NN design to tackling remaining classification uncertainties caused by areas of text overlap, inhomogeneity, and peaks of degradation. Specifically, we introduce a new output class for pixels within overlapping text areas and incorporate additional features related to the pixel context information to promote the same classification for pixels adjacent to each other. Our experiments demonstrate that these enhancements significantly improve the classification accuracy. This improvement is evident in the quality of both binarization, which aids in text analysis, and virtual restoration, aimed at recovering the manuscript’s original appearance. Tests conducted on a public dataset, using standard quality indices, reveal that the proposed method outperforms both our previous proposals and other notable methods found in the literature.Source: MATHEMATICS, vol. 12 (issue 21), p. 3402
DOI: 10.3390/math12213402
Metrics:
Savino P., Tonazzini A.
A common cause of deterioration in historic manuscripts is ink transparency or bleeding from the opposite page. Philologists and paleographers can significantly benefit from minimizing these interferences when attempting to decipher the original text. Additionally, computer-aided text analysis can also gain from such text enhancement. In previous work, we proposed the use of neural networks (NNs) in combination with a data model that characterizes the damage when both sides of a page have been digitized. This approach offers the distinct advantage of allowing the creation of an artificial training set that teaches the NN to differentiate between clean and damaged pixels. We tested this concept using a shallow NN, which proved effective in categorizing texts with varying levels of deterioration. In this study, we adapt the NN design to tackling remaining classification uncertainties caused by areas of text overlap, inhomogeneity, and peaks of degradation. Specifically, we introduce a new output class for pixels within overlapping text areas and incorporate additional features related to the pixel context information to promote the same classification for pixels adjacent to each other. Our experiments demonstrate that these enhancements significantly improve the classification accuracy. This improvement is evident in the quality of both binarization, which aids in text analysis, and virtual restoration, aimed at recovering the manuscript’s original appearance. Tests conducted on a public dataset, using standard quality indices, reveal that the proposed method outperforms both our previous proposals and other notable methods found in the literature.Source: MATHEMATICS, vol. 12 (issue 21), p. 3402
DOI: 10.3390/math12213402
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Mathematics | IRIS Cnr | IRIS Cnr | IRIS Cnr | CNR IRIS
2023
Journal article
Open Access
Restoration and content analysis of ancient manuscripts via color space based segmentation
Hanif M, Tonazzini A, Hussain Sf, 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, vol. 18
DOI: 10.1371/journal.pone.0282142
Metrics:
Hanif M, Tonazzini A, Hussain Sf, 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, vol. 18
DOI: 10.1371/journal.pone.0282142
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PLoS ONE | CNR IRIS | journals.plos.org | ISTI Repository | CNR IRIS
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.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.DOI: 10.1007/978-3-031-37117-2_37
Metrics:
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CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Journal article
Open Access
Blind bleed-through removal in color ancient manuscripts
Hanif M, Tonazzini A, Hussain Sf, 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), vol. 82, pp. 12321-12335
DOI: 10.1007/s11042-022-13755-6
DOI: https://doi.org/10.1007/s11042-022-13755-6
Metrics:
Hanif M, Tonazzini A, Hussain Sf, 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), vol. 82, pp. 12321-12335
DOI: 10.1007/s11042-022-13755-6
DOI: https://doi.org/10.1007/s11042-022-13755-6
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CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
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.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.DOI: 10.1002/9781119759614.ch8
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CNR IRIS | onlinelibrary.wiley.com | ISTI Repository | CNR IRIS
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: CEUR WORKSHOP PROCEEDINGS. 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: CEUR WORKSHOP PROCEEDINGS. Online event, 12/09/2022
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ceur-ws.org | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS
2022
Other
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.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/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.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
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CNR IRIS | ISTI Repository | CNR IRIS
2021
Conference article
Restricted
Digital safeguard of laminated historical manuscripts: the treatise "Poem in Rajaz on medicine" as a case study
Del Grosso Am, 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.DOI: 10.1109/cist49399.2021.9357192
Metrics:
Del Grosso Am, 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.DOI: 10.1109/cist49399.2021.9357192
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doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
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, vol. 7 (issue 3)
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, vol. 7 (issue 3)
DOI: 10.3390/jimaging7030053
Metrics:
See at:
CNR IRIS | ISTI Repository | DOAJ-Articles | Journal of Imaging | CNR IRIS
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, vol. 10 (issue 4), p. 338
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, vol. 10 (issue 4), p. 338
DOI: 10.3390/biology10040338
Metrics:
See at:
Europe PubMed Central | CNR IRIS | ISTI Repository | www.mdpi.com | Biology | CNR IRIS
2021
Other
Open Access
SI-Lab Annual Research Report 2020
Leone Gr, Righi M, Carboni A, Caudai C, Colantonio S, Kuruoglu Ee, Leporini B, Magrini M, Paradisi P, Pascali Ma, 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.DOI: https://doi.org/10.32079/isti-ar-2021/001
DOI: 10.32079/isti-ar-2021/001
Metrics:
Leone Gr, Righi M, Carboni A, Caudai C, Colantonio S, Kuruoglu Ee, Leporini B, Magrini M, Paradisi P, Pascali Ma, 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.DOI: https://doi.org/10.32079/isti-ar-2021/001
DOI: 10.32079/isti-ar-2021/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Journal article
Open Access
Challenges in the digital analysis of historical laminated manuscripts
Del Grosso Am, Fihri Df, 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, vol. 5 (issue 1), pp. 34-43
DOI: 10.57675/imist.prsm/ijist-v5i1.190
Metrics:
Del Grosso Am, Fihri Df, 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, vol. 5 (issue 1), pp. 34-43
DOI: 10.57675/imist.prsm/ijist-v5i1.190
Metrics:
See at:
CNR IRIS | innove.org | ISTI Repository | International Journal of Information Science and Technology | doi.org | CNR IRIS
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
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
DOI: 10.1142/s2196888822500099
Metrics:
See at:
CNR IRIS | ISTI Repository | ISTI Repository | www.worldscientific.com | Vietnam Journal of Computer Science | CNR IRIS | CNR IRIS
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 Mv, Tonazzini A, Torre M, Triolo Paolo Am, Zantedeschi G
This work proposes a methodology of statistical analysis of diagnostic images aimed at improving their reading and facilitating the graphic transcription of the state of conservation of artistic assets, making it punctual and repeatable. As a case study we present a small oil painting on canvas of an anonymous author in a bad state of conservation. Using the proposed methodology, based on a semi-automatic approach of extraction of the areas of interest, we obtain several survey sheets related to the conservation status through which it is possible to perform zonal statistics to compute the percentage of damage. The operations shown can be applied to any type of diagnostic image, studying more objectively the state of conservation of any artifact.Source: KERMES, vol. Anno XXXIV (issue 121), pp. 17-24
Amura A, Aldini A, Landi L, Pisani L, Salerno E, Soro Mv, Tonazzini A, Torre M, Triolo Paolo Am, Zantedeschi G
This work proposes a methodology of statistical analysis of diagnostic images aimed at improving their reading and facilitating the graphic transcription of the state of conservation of artistic assets, making it punctual and repeatable. As a case study we present a small oil painting on canvas of an anonymous author in a bad state of conservation. Using the proposed methodology, based on a semi-automatic approach of extraction of the areas of interest, we obtain several survey sheets related to the conservation status through which it is possible to perform zonal statistics to compute the percentage of damage. The operations shown can be applied to any type of diagnostic image, studying more objectively the state of conservation of any artifact.Source: KERMES, vol. Anno XXXIV (issue 121), pp. 17-24
See at:
CNR IRIS | ISTI Repository | www.kermes-restauro.it | CNR IRIS
2021
Journal article
Open Access
Special Issue on Digital preservation of Written Heritage and Text Processing Technology - Preface
Ouafae Nahli, Anna Tonazzini, Stefano Legnaioli
[Extract] The notion of “written heritage” concerns the immense field regarding written culture. It is difficult to attempt a definition of written heritage. In very general terms, it can be said that it includes any surface containing something written. Different writing supports can be involved, such as papyri, parchments, manuscripts, books, but also contemporary type of medium, such as posters, newspapers, magazines, etc. The written cultural heritage such as general written records are means of communication through space and time of costumes, culture, and beliefs. They constitute the archive of the evolution of mentality, knowledge, sciences, and arts and are considered guardians of the languageSource: INTERNATIONAL JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 5 (issue 1), pp. 1-4
Ouafae Nahli, Anna Tonazzini, Stefano Legnaioli
[Extract] The notion of “written heritage” concerns the immense field regarding written culture. It is difficult to attempt a definition of written heritage. In very general terms, it can be said that it includes any surface containing something written. Different writing supports can be involved, such as papyri, parchments, manuscripts, books, but also contemporary type of medium, such as posters, newspapers, magazines, etc. The written cultural heritage such as general written records are means of communication through space and time of costumes, culture, and beliefs. They constitute the archive of the evolution of mentality, knowledge, sciences, and arts and are considered guardians of the languageSource: INTERNATIONAL JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 5 (issue 1), pp. 1-4
See at:
CNR IRIS | www.innove.org | CNR IRIS
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 SCIENZE DEL COLORE / COLOR CULTURE AND SCIENCE, vol. 12 (issue 2), pp. 7-15
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 SCIENZE DEL COLORE / COLOR CULTURE AND SCIENCE, vol. 12 (issue 2), pp. 7-15
DOI: 10.23738/ccsj.120201
Metrics:
See at:
CNR IRIS | jcolore.gruppodelcolore.it | ISTI Repository | CNR IRIS