2023
Contribution to conference
Open Access
Evaluating the velocity of ships from low resolution SAR images
Reggiannini M., Salerno E.
An abstract is not evaluableSource: ICEAA 2023 - 24th International Conference on Electromagnetics in Advanced Applications, pp. 328–328, Venice, Italy, 9-13/10/2023
DOI: 10.1109/iceaa57318.2023.10297866
Metrics:
Reggiannini M., Salerno E.
An abstract is not evaluableSource: ICEAA 2023 - 24th International Conference on Electromagnetics in Advanced Applications, pp. 328–328, Venice, Italy, 9-13/10/2023
DOI: 10.1109/iceaa57318.2023.10297866
Metrics:
See at:
ISTI Repository 
| ieeexplore.ieee.org 
| CNR ExploRA
2023
Journal article
Unknown
Remote sensing for maritime traffic understanding
Reggiannini M., Salerno E., Bacciu C., D'Errico A., Lo Duca A., Marchetti A., Martinelli M., Mercurio C., Mistretta A., Righi M., Tampucci M., Di Paola C.
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: Remote sensing (Basel) (2023).
Reggiannini M., Salerno E., Bacciu C., D'Errico A., Lo Duca A., Marchetti A., Martinelli M., Mercurio C., Mistretta A., Righi M., Tampucci M., Di Paola C.
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: Remote sensing (Basel) (2023).
See at: CNR ExploRA
2023
Report
Open Access
Digital Humanities: mission accomplished? A scholarly literature analysis
Salerno E.
Digital Humanities have been evolving throughout the parallel evolution of computers, software and networking techniques, as well as the different attitudes of the interested scholars. Since the earliest historical phases of this research field, scholars have been debating on whether it can be considered to be a new academic discipline and whether it is revolutionary in nature. About twenty years ago, the early denotation of Humanities Computing evolved to the present one, and deep changes intervened in digital information technologies, as well as in their humanities applications. This paper accounts for the relevant scholarly debate, distinguishing between the early period and the most recent years, then tries to frame this process in a model of scientific revolution.Source: ISTI Working papers, 2023
DOI: 10.13140/rg.2.2.27110.19528
Metrics:
Salerno E.
Digital Humanities have been evolving throughout the parallel evolution of computers, software and networking techniques, as well as the different attitudes of the interested scholars. Since the earliest historical phases of this research field, scholars have been debating on whether it can be considered to be a new academic discipline and whether it is revolutionary in nature. About twenty years ago, the early denotation of Humanities Computing evolved to the present one, and deep changes intervened in digital information technologies, as well as in their humanities applications. This paper accounts for the relevant scholarly debate, distinguishing between the early period and the most recent years, then tries to frame this process in a model of scientific revolution.Source: ISTI Working papers, 2023
DOI: 10.13140/rg.2.2.27110.19528
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Unknown
Testing random-forest models trained by Sentinel-1 data from the OpenSARShip data set
Salerno E.
We explore the capabilities of random forest models to classify several types of ships imaged through a satellite-borne C-band SAR with 20m spatial resolution. A number of attribute subsets estimated from the Sentinel 1 images provided by the OpenSARShip public data set are used to train models that are then tested against never-seen-before data. A vast data set has been extracted from OpenSARShip and used to estimate the whole attribute set, composed of 8 naive geometrical features and 8 scattering features. The results are encouraging, as the performances obtained seem to be good when compared to other results from non-deep-learning classifiers reported in the literature. Against previous claims found in the literature, the advantages of adding scattering features to purely geometric ones is here confirmed.Source: ISTI Working papers, 2022
Salerno E.
We explore the capabilities of random forest models to classify several types of ships imaged through a satellite-borne C-band SAR with 20m spatial resolution. A number of attribute subsets estimated from the Sentinel 1 images provided by the OpenSARShip public data set are used to train models that are then tested against never-seen-before data. A vast data set has been extracted from OpenSARShip and used to estimate the whole attribute set, composed of 8 naive geometrical features and 8 scattering features. The results are encouraging, as the performances obtained seem to be good when compared to other results from non-deep-learning classifiers reported in the literature. Against previous claims found in the literature, the advantages of adding scattering features to purely geometric ones is here confirmed.Source: ISTI Working papers, 2022
See at: CNR ExploRA
2022
Journal article
Open Access
Using low-resolution SAR scattering features for ship classification
Salerno E.
This letter reports an experimental study aimed at establishing the questionable usefulness of scattering attributes for ship classification from moderate-resolution SAR images. About 2700 example images representing four ship types have been extracted from the OpenSARShip annotated data set and used to form the training and test sets for random forest models. After importance ranking and cross-validation, different subsets of both geometric and scattering attributes were selected from a fixed training set and used to train the classifier. The results from the validation using the test sets show that the scattering attributes give a significant contribution in terms of overall classification accuracy.Source: IEEE geoscience and remote sensing letters (Online) 19 (2022). doi:10.1109/LGRS.2022.3183622
DOI: 10.1109/lgrs.2022.3183622
Metrics:
Salerno E.
This letter reports an experimental study aimed at establishing the questionable usefulness of scattering attributes for ship classification from moderate-resolution SAR images. About 2700 example images representing four ship types have been extracted from the OpenSARShip annotated data set and used to form the training and test sets for random forest models. After importance ranking and cross-validation, different subsets of both geometric and scattering attributes were selected from a fixed training set and used to train the classifier. The results from the validation using the test sets show that the scattering attributes give a significant contribution in terms of overall classification accuracy.Source: IEEE geoscience and remote sensing letters (Online) 19 (2022). doi:10.1109/LGRS.2022.3183622
DOI: 10.1109/lgrs.2022.3183622
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | 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
Metrics:
See at:
ISTI Repository | CNR ExploRA
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
Metrics:
See at:
onlinelibrary.wiley.com | 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
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Unknown
Using random forests to classify vessels from naive geometrical features
Salerno E.
This report is concerned with the application of Random Forest classification methods to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the theory and and the features of this class of methods, we select an R package useful to train, test and execute the classifier. Some experiments are then reported using naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this classifier is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
Salerno E.
This report is concerned with the application of Random Forest classification methods to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the theory and and the features of this class of methods, we select an R package useful to train, test and execute the classifier. Some experiments are then reported using naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this classifier is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
See at: CNR ExploRA
2021
Report
Unknown
Naive bayes for naive geometry: classifying vessels from length and beam
Salerno E.
This report is concerned with the application of a Naive Bayes classification method to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the principles behind the method, a simple implementation and an extensive experimentation on naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set are presented. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this Naive Bayes is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
Salerno E.
This report is concerned with the application of a Naive Bayes classification method to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the principles behind the method, a simple implementation and an extensive experimentation on naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set are presented. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this Naive Bayes is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
See at: CNR ExploRA
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
Metrics:
See at:
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
Metrics:
See at:
Europe PubMed Central | ISTI Repository | www.mdpi.com | Biology | CNR ExploRA
2021
Report
Unknown
Multiple kernel learning to classify vessels from naive geometrical features
Salerno E.
This report is concerned with the application of a Multiple Kernel Learning classification method to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the theory and and the features of this class of methods, we select a few R packages useful to this aim, and delineate a procedure to select the relevant features and kernel functions, execute and test the classifier. Some experiments are then reported using naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this classifier is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
Salerno E.
This report is concerned with the application of a Multiple Kernel Learning classification method to the identification of ship types in moderate-resolution SAR images. After a brief presentation of the theory and and the features of this class of methods, we select a few R packages useful to this aim, and delineate a procedure to select the relevant features and kernel functions, execute and test the classifier. Some experiments are then reported using naive geometrical features extracted from a few thousands of targets in the OpenSARShip data set. All the ship chips extracted are derived from IW GRD Sentinel 1 C-band SAR images, accompanied by AIS and MarineTraffic ground-truth data. The ideal performance of this classifier is evaluated through the standard classification indices, with respect to the ship types that are sufficiently represented in the subsets considered.Source: ISTI Working papers, 2021
See at: 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:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Unknown
OSIRIS-FO - OSIRIS PDR Meeting - CNR-ISTI current status
Salerno E., Martinelli M., Reggiannini M., Righi M., Tampucci M.
ESA OSIRIS 2 Project - Current status of CNR-ISTISource: ISTI Project report, OSIRIS-FO, 2021
Salerno E., Martinelli M., Reggiannini M., Righi M., Tampucci M.
ESA OSIRIS 2 Project - Current status of CNR-ISTISource: ISTI Project report, OSIRIS-FO, 2021
See at: CNR ExploRA
2021
Journal article
Open Access
AI applications in functional genomics
Caudai C., Galizia A., Geraci F., Le Pera L., Morea V., Salerno E., Via A., Colombo T.
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by ''deep learning", along with a burst of ''big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.Source: Computational and Structural Biotechnology Journal 19 (2021): 5762–5790. doi:10.1016/j.csbj.2021.10.009
DOI: 10.1016/j.csbj.2021.10.009
Metrics:
Caudai C., Galizia A., Geraci F., Le Pera L., Morea V., Salerno E., Via A., Colombo T.
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by ''deep learning", along with a burst of ''big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.Source: Computational and Structural Biotechnology Journal 19 (2021): 5762–5790. doi:10.1016/j.csbj.2021.10.009
DOI: 10.1016/j.csbj.2021.10.009
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2021
Report
Unknown
Geometric and scattering features for ship classification from Sentinel 1 SAR images
Salerno E.
Following the evaluation of some ship classification strategies based on geometrical features, this report accounts for the use of scattering measurements in SAR images as additional features, in the hope of improving the classification performance. A set of eight scattering features has been selected and added to the already tested set of eight naive geometric features to explore the discriminating power of the whole feature set or any subset thereof. The algorithm chosen for this investigation is Random Forest, as implemented in the R package randomForest. The basic finding has been that, as opposed to some claims in the literature, the use of scattering features improves the classification performance even from images characterized by a moderate resolution, such as the ones provided by ESA's Sentinel 1 satellite-borne SAR.Source: ISTI Working papers, 2021
Salerno E.
Following the evaluation of some ship classification strategies based on geometrical features, this report accounts for the use of scattering measurements in SAR images as additional features, in the hope of improving the classification performance. A set of eight scattering features has been selected and added to the already tested set of eight naive geometric features to explore the discriminating power of the whole feature set or any subset thereof. The algorithm chosen for this investigation is Random Forest, as implemented in the R package randomForest. The basic finding has been that, as opposed to some claims in the literature, the use of scattering features improves the classification performance even from images characterized by a moderate resolution, such as the ones provided by ESA's Sentinel 1 satellite-borne SAR.Source: ISTI Working papers, 2021
See at: 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
Report
Unknown
Uso di tecniche di sparse independent component analysis per l'estrazione di regioni di interesse in opere pittoriche e grafiche
Salerno E.
In questa nota si mostra come, in certe applicazioni legate alle tecnologie dell'informazione per lo studio del patrimonio culturale, possano essere applicati metodi di separazione cieca delle componenti basati sulla sparsità e non sull'indipendenza statistica. Nelle applicazioni in cui sia necessario estrarre da immagini di opere pittoriche o manoscritti delle regioni di interesse isolate spazialmente, le condizioni di sparsità sono teoricamente verificate già nello spazio delle immagini, e non occorre passare a uno spazio trasformato per poterle imporre alla soluzione del problema. Da due algoritmi recentemente proposti in letteratura, sono stati derivati e sperimentati i corrispondenti operanti direttamente nello spazio delle immagini. Uno di essi impone solo il requisito di sparsità, mentre l'altro aggiunge anche un vincolo di incorrelazione. Gli esperimenti sono condotti su due immagini reali, una relativa a un dipinto acquisito nel visibile e nell'infrarosso e una a un manoscritto acquisito su entrambe le facce nelle tre bande rossa, verde e blu dello spettro visibile.Source: ISTI Working papers, 2020
Salerno E.
In questa nota si mostra come, in certe applicazioni legate alle tecnologie dell'informazione per lo studio del patrimonio culturale, possano essere applicati metodi di separazione cieca delle componenti basati sulla sparsità e non sull'indipendenza statistica. Nelle applicazioni in cui sia necessario estrarre da immagini di opere pittoriche o manoscritti delle regioni di interesse isolate spazialmente, le condizioni di sparsità sono teoricamente verificate già nello spazio delle immagini, e non occorre passare a uno spazio trasformato per poterle imporre alla soluzione del problema. Da due algoritmi recentemente proposti in letteratura, sono stati derivati e sperimentati i corrispondenti operanti direttamente nello spazio delle immagini. Uno di essi impone solo il requisito di sparsità, mentre l'altro aggiunge anche un vincolo di incorrelazione. Gli esperimenti sono condotti su due immagini reali, una relativa a un dipinto acquisito nel visibile e nell'infrarosso e una a un manoscritto acquisito su entrambe le facce nelle tre bande rossa, verde e blu dello spettro visibile.Source: ISTI Working papers, 2020
See at: CNR ExploRA
2020
Report
Unknown
Integration of analysis of the hierarchical process and dempster-shafer theory for cooperative evaluation tasks
Salerno E.
This note gives some details on the application of Saaty's Analysis of Hierarchy Process and the Dempster-Shafer theory for an evaluation problem that embeds a multicriterion decision and an expert judgement on a number of value indicators. These two tasks are assumed to be entitled to two groups of experts. The judgement matrices issued by the first group are geometrically averaged and the related criteria are prioritized by the analysis of hierarchy process. Then, the judgements from the second group of experts are translated into a fuzzy language and fused through the Dempster-Shafer theory. Finally, the masses resulting from this process are propagated up the hierarchy using the previously computed priorities.Source: ISTI Working papers, 2020
Salerno E.
This note gives some details on the application of Saaty's Analysis of Hierarchy Process and the Dempster-Shafer theory for an evaluation problem that embeds a multicriterion decision and an expert judgement on a number of value indicators. These two tasks are assumed to be entitled to two groups of experts. The judgement matrices issued by the first group are geometrically averaged and the related criteria are prioritized by the analysis of hierarchy process. Then, the judgements from the second group of experts are translated into a fuzzy language and fused through the Dempster-Shafer theory. Finally, the masses resulting from this process are propagated up the hierarchy using the previously computed priorities.Source: ISTI Working papers, 2020
See at: CNR ExploRA