- Curriculum Vitae :
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| Author | Title | Year | Journal/Proceedings | URL | |
|---|---|---|---|---|---|
| Binaghi, E., Boschetti, M., Brivio, P., Gallo, I., Pergalani, F. & Rampini, A. | Prediction of Displacements in Unstable Areas Using a
Neural Model
[BibTeX] |
2004 | NATURAL HAZARDS |  |
|
BibTeX:
@article{Binaghi2004prediction,
author = {E. Binaghi and M. Boschetti and P.A. Brivio and I. Gallo and F. Pergalani and A. Rampini},
title = {Prediction of Displacements in Unstable Areas Using a Neural Model},
journal = {NATURAL HAZARDS},
year = {2004},
pages = {135--154}
}
|
|||||
| Binaghi, E., Brivio, P., Gallo, I., Pepe, M. & Rampini, A. | Recent Developments in Pattern Recognition Research
[BibTeX] |
2000 | |||
BibTeX:
@inbook{Binaghi2000remote,
author = {E. Binaghi and P.A. Brivio and I. Gallo and M. Pepe and A. Rampini},
title = {Recent Developments in Pattern Recognition Research},
publisher = {Transworld Research Network Publishing},
year = {2000},
pages = {89--112}
}
|
|||||
| Binaghi, E., Brivio, P., Gallo, I., Pepe, M. & Rampini, A. | Riconoscimento automatico dell'urbano in ortofoto
digitali a colori
[BibTeX] |
2000 | |||
BibTeX:
@inproceedings{Binaghi2000urbano,
author = {E. Binaghi and P.A. Brivio and I. Gallo and M. Pepe and A. Rampini},
title = {Riconoscimento automatico dell'urbano in ortofoto digitali a colori},
year = {2000},
volume = {1},
pages = {209--216}
}
|
|||||
| Binaghi, E., Carullo, M., Gallo, I. & Madaio, M. | Text Categorization of Commercial Web Pages
[BibTeX] |
2008 | The IASTED Conference on Artificial Intelligence and Applications, Innsbruck, Austria | |
|
BibTeX:
@inproceedings{binaghi2008text,
author = {E. Binaghi and M. Carullo and I. Gallo and M. Madaio},
title = {Text Categorization of Commercial Web Pages},
booktitle = {The IASTED Conference on Artificial Intelligence and Applications, Innsbruck, Austria},
year = {2008},
pages = {7--11}
}
|
|||||
| Binaghi, E., Gallo, I., Baraldi, A. & Gerhardinger, A. | Neural Disparity Computation from IKONOS Stereo Imagery
in the Presence of Occlusions
[BibTeX] |
2006 | |||
BibTeX:
@inproceedings{Binaghi2006stereo,
author = {E. Binaghi and I. Gallo and A. Baraldi and A. Gerhardinger},
title = {Neural Disparity Computation from IKONOS Stereo Imagery in the Presence of Occlusions},
year = {2006},
volume = {6365},
pages = {63650B-1--63650B-11}
}
|
|||||
| Binaghi, E., Gallo, I., Boschetti, M. & Brivio, P. | A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data | 2005 | Image Analysis and Processing-ICIAP 2005 | DOI
|
|
| Abstract: In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a network pruning algorithm acting on MultiLayer Perceptron topology is the foundation of the feature selection strategy. Feature selection is implemented within the back-propagation learning process and based on a measure of saliency derived from bell functions positioned between input and hidden layers and adaptively varied in shape and position during learning. Performances were evaluated experimentally within a Remote Sensing study, aimed to classify hyperspectral data. A comparison analysis was conducted with Support Vector Machine and conventional statistical and neural techniques. As seen in the experimental context, the adaptive neural classifier showed a competitive behavior with respect to the other classifiers considered; it performed a selection of the most relevant features and showed a robust behavior operating under minimal training and noisy situations. | |||||
BibTeX:
@inproceedings{Binaghi2005features,
author = {E. Binaghi and I. Gallo and M. Boschetti and P.A. Brivio},
title = {A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data},
year = {2005},
volume = {3617},
pages = {753--760}
}
|
|||||
| Binaghi, E., Gallo, I., Boschetti, M. & Brivio, P. | A neural adaptive model for hyperspectral data
classification under minimal training conditions
[BibTeX] |
2004 | |||
BibTeX:
@inproceedings{Binaghi2004classification,
author = {E. Binaghi and I. Gallo and M. Boschetti and P.A. Brivio},
title = {A neural adaptive model for hyperspectral data classification under minimal training conditions},
year = {2004},
volume = {5573},
pages = {173--181}
}
|
|||||
| Binaghi, E., Gallo, I., Boschetti, M. & Brivio, P. | Spectral/Spatial Data Fusion and Neural Networks for
Vegetation Understory Information Extraction from Hyperspectral
Airborne Images
[BibTeX] |
2003 | |||
BibTeX:
@inproceedings{Binaghi2003fusion,
author = {E. Binaghi and I. Gallo and M. Boschetti and P.A. Brivio},
title = {Spectral/Spatial Data Fusion and Neural Networks for Vegetation Understory Information Extraction from Hyperspectral Airborne Images},
year = {2003},
volume = {5238},
pages = {288--296}
}
|
|||||
| Binaghi, E., Gallo, I., Brivio, P., Musazzi, S. & Bassini, A. | Neural classification of high resolution remote sensing
imagery for power transmission lines surveillance
[BibTeX] |
2002 | |||
BibTeX:
@inproceedings{Binaghi2002powerlines,
author = {E. Binaghi and I. Gallo and P.A. Brivio and S. Musazzi and A. Bassini},
title = {Neural classification of high resolution remote sensing imagery for power transmission lines surveillance},
year = {2002},
pages = {500--502}
}
|
|||||
| Binaghi, E., Gallo, I., Fornasier, C. & Raspanti, M. | Growing Aggregation Algorithm for Dense Two-Frame
Stereo Correspondence
[BibTeX] |
2006 | |||
BibTeX:
@inproceedings{Binaghi2006growing,
author = {E. Binaghi and I. Gallo and C. Fornasier and M. Raspanti},
title = {Growing Aggregation Algorithm for Dense Two-Frame Stereo Correspondence},
year = {2006},
pages = {326--332}
}
|
|||||
| Binaghi, E., Gallo, I., Ghiselli, C., Levrini, L. & Biondi, K. | An integrated fuzzy logic and web-based framework for active protocol support. | 2007 | Int J Med Inform | DOI | |
| Abstract: OBJECTIVE: To develop a general purpose web-based system for active support in using protocols. METHODS: The conceptual model underlying the design of the overall decision support system is drawn from fuzzy set theory and fuzzy logic giving rise to a framework for both acquiring and representing descriptive and operational protocol knowledge. It has been conceived as a web application, designed and implemented according to the technological standards of Internet, in particular XML language and web services, to guarantee distributed functionalities for multicentric studies and the re-use of domain knowledge and problem solving strategies. RESULTS: Solutions have been evaluated experimentally addressing the specific domain of Temporomandibular disorders (TMD) specializing the general purpose procedures to the dedicated "Research Diagnostic Criteria" (RDC)/TMD protocol. The accuracy of the system was correlated with a set of 50 clinically cases consecutively selected from the Gnatology Department. The results were consistent in 100% of cases. The systematic observation by physicians of both activated rules and diagnostic judgements identified and explicitly formalized additional diagnostic rules in the same context. CONCLUSIONS: Active protocol support based on fuzzy diagnostic reasoning and advanced web-based technologies shows great potentialities for the renewed role DSSs are called to play in the increasingly wide scale context of evidence-based medicine. | |||||
BibTeX:
@article{Binaghi2007integrated,
author = {Elisabetta Binaghi and Ignazio Gallo and Cristina Ghiselli and Luca Levrini and Katia Biondi},
title = {An integrated fuzzy logic and web-based framework for active protocol support.},
journal = {Int J Med Inform},
year = {2007},
doi = {10.1016/j.ijmedinf.2007.06.004}
}
|
|||||
| Binaghi, E., Gallo, I., Guidali, A., Raspanti, M. & Salvini, G. | Adaptive Neural Regularization Assignment for
Semi-Blind Biomedical Image Restoration
[BibTeX] |
2007 | |||
BibTeX:
@inproceedings{Binaghi2007adaptive,
author = {E. Binaghi and I. Gallo and A. Guidali and M. Raspanti and G. Salvini},
title = {Adaptive Neural Regularization Assignment for Semi-Blind Biomedical Image Restoration},
year = {2007},
pages = {207--207}
}
|
|||||
| Binaghi, E., Gallo, I., Lanzarone, G. A. & Pepe, M. | Geospatial Pattern Recognition
[BibTeX] |
2002 | |||
BibTeX:
@inbook{Binaghi2002pyramid,
author = {E. Binaghi and I. Gallo and G. A. Lanzarone and M. Pepe},
title = {Geospatial Pattern Recognition},
publisher = {Research Signpost},
year = {2002},
pages = {87--103}
}
|
|||||
| Binaghi, E., Gallo, I. & Madella, P. | A Neural Model for Fuzzy Dempster-Shafer Classifiers | 2000 | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | DOI | |
| Abstract: This paper presents a supervised classification model integrating fuzzy reasoning and Dempster–Shafer propagation of evidence has been built on top of connectionist techniques to address classification tasks in which vagueness and ambiguity coexist. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster–Shafer theory. In this context the learning task can be formulated as the search for the most adequate “ingredients� of the fuzzy and Dempster–Shafer frameworks such as the fuzzy aggregation operators, for fusing data from different sources and focal elements, and basic probability assignments, describing the contributions of evidence in the inference scheme. The new neural model allows us to establish a complete correspondence between connectionist elements and fuzzy and Dempster–Shafer ingredients, ensuring both a high level of interpretability, and transparency and high performance in classification. Experiments with simulated data show that the network can cope well with problems of different complexity. The experiments with real data show the superiority of the neural implementation with respect to the symbolic representation, and prove that the integration of the propagation of evidence provides better classification results and fuzzy reasoning within connectionist schema than those obtained by pure neuro-fuzzy models. | |||||
BibTeX:
@article{Binaghi2000dempster,
author = {E. Binaghi and I. Gallo and P. Madella},
title = {A Neural Model for Fuzzy Dempster-Shafer Classifiers},
journal = {INTERNATIONAL JOURNAL OF APPROXIMATE REASONING},
year = {2000},
volume = {25},
pages = {89--121},
doi = {http://dx.doi.org/10.1016/S0888-613X(00)00050-5}
}
|
|||||
| Binaghi, E., Gallo, I., Madella, P., Pepe, M. & Rampini, A. | Uncertainty Management in Neural Classifiers of Remotely Sensed Data | 1999 | DOI | ||
| Abstract: This paper presents a novel neural model based on back-propagation for fuzzy Dempster-Shafer (FDS) classifiers. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster-Shafer theory. In this context the learning task may be formulated as the search the most adequate ingredients of the fuzzy and Dempster-Shafer frameworks such as the fuzzy aggregation operators for fusing data from different sources and focal elements and basic probability assignments for describing the contributions of evidence in the inference scheme. The new neural model allows to establish a complete correspondence between connectionist elements and fuzzy and Dempster-Shafer ingredients ensuring both an high level of interpretability and transparency and high performances in classification. | |||||
BibTeX:
@inproceedings{Binaghi1999uncertainty,
author = {E. Binaghi and I. Gallo and P. Madella and M. Pepe and A. Rampini},
title = {Uncertainty Management in Neural Classifiers of Remotely Sensed Data},
year = {1999},
volume = {3871},
pages = {195--204},
doi = {10.1117/12.373258}
}
|
|||||
| Binaghi, E., Gallo, I., Madella, P. & Rampini, A. | Information Processing for Remote Sensing
[BibTeX] |
1999 | |||
BibTeX:
@inbook{Binaghi1999approxiamte,
author = {E. Binaghi and I. Gallo and P. Madella and A. Rampini},
title = {Information Processing for Remote Sensing},
publisher = {World Scientific Publishing Co.},
year = {1999},
pages = {397--429}
}
|
|||||
| Binaghi, E., Gallo, I. & Pepe, M. | A neural adaptive model for feature extraction and
recognition in high resolution remote sensing imagery
[BibTeX] |
2003 | INTERNATIONAL JOURNAL OF REMOTE SENSING | ||
BibTeX:
@article{Binaghi2003adaptive,
author = {E. Binaghi and I. Gallo and M. Pepe},
title = {A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery},
journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
year = {2003},
volume = {24},
pages = {3947--3959}
}
|
|||||
| Binaghi, E., Gallo, I. & Pepe, M. | Integration of Scale-Space Filtering and Neural
Techniques for High Resolution Remote Sensing Image Classification
[BibTeX] |
2003 | |||
BibTeX:
@inproceedings{Binaghi2003filtering,
author = {E. Binaghi and I. Gallo and M. Pepe},
title = {Integration of Scale-Space Filtering and Neural Techniques for High Resolution Remote Sensing Image Classification},
year = {2003},
volume = {4885},
pages = {469--476}
}
|
|||||
| Binaghi, E., Gallo, I. & Pepe, M. | Frontiers of Remote Sensing Information Processing
[BibTeX] |
2003 | |||
BibTeX:
@inbook{Binaghi2003powerlines,
author = {E. Binaghi and I. Gallo and M. Pepe},
title = {Frontiers of Remote Sensing Information Processing},
year = {2003},
pages = {?--?}
}
|
|||||
| Binaghi, E., Gallo, I. & Pepe, M. | A cognitive pyramid for contextual classification
remote sensing images
[BibTeX] |
2003 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | ||
BibTeX:
@article{Binaghi2003pyramid,
author = {E. Binaghi and I. Gallo and M. Pepe},
title = {A cognitive pyramid for contextual classification remote sensing images},
journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
year = {2003},
volume = {41},
pages = {2906--2922}
}
|
|||||
| Binaghi, E., Gallo, I., Raspanti, M. & Marino, G. | Neural adaptive stereo matching
[BibTeX] |
2004 | PATTERN RECOGNITION LETTERS | DOI |
|
| Abstract: The present work investigates the potential of neural adaptive learning to solve the correspondence problem within a two-frame adaptive area matching approach. A novel method is proposed based on the use of the zero mean normalized cross-correlation coefficient integrated within a neural network model which uses a least-mean-square delta rule for training.Two experiments were conducted for evaluating the neural model proposed. The first aimed to produce dense disparity maps based on the analysis of standard test images. The second experiment, conducted in the biomedical field, aimed to model 3D surfaces from a varied set of scanning electron microscope stereoscopic image pairs. | |||||
BibTeX:
@article{Binaghi2004stereo,
author = {E. Binaghi and I. Gallo and M. Raspanti and G. Marino},
title = {Neural adaptive stereo matching},
journal = {PATTERN RECOGNITION LETTERS},
year = {2004},
volume = {25},
pages = {1743--1758}
}
|
|||||
| Binaghi, E., Madella, P., Gallo, I. & Rampini, A. | A Neural Refinement Strategy For Fuzzy Dempster-Shafer Classifier of Multisource Remote Sensing Images | 1998 | DOI | ||
| Abstract: This paper presents a hybrid strategy for the classification of multisource remote sensing images basing on a knowledge representation framework which integrates fuzzy logic and Dempster-Shafer theory and is capable of dealing with possibilistic and credibilistic forms of uncertainty in an unified way. Within the strategy, the salient, innovative aspect here proposed is the use of a novel neural network model for refinement of fuzzy Dempster-Shafer classification rules. The approach has been evaluated by developing real- world applications in the field of water vulnerability assessment and fire risk assessment. Numerical results obtained show that classification benefit from the integration of neural and symbolic frameworks. | |||||
BibTeX:
@inproceedings{Binaghi1998fuzzy,
author = {E. Binaghi and P. Madella and I. Gallo and A. Rampini},
title = {A Neural Refinement Strategy For Fuzzy Dempster-Shafer Classifier of Multisource Remote Sensing Images},
year = {1998},
volume = {3500},
pages = {214--224},
doi = {10.1117/12.331866}
}
|
|||||
| Binaghi, E., P., A. B., Gallo, I., Pepe, M. & Rampini, A. | Robust recognition of urban patterns using a two stage
soft - hard neural classification
[BibTeX] |
2001 | |||
BibTeX:
@inproceedings{Binaghi2001soft,
author = {E. Binaghi and A. Brivio P. and I. Gallo and M. Pepe and A. Rampini},
title = {Robust recognition of urban patterns using a two stage soft - hard neural classification},
year = {2001},
volume = {4170},
pages = {49--56}
}
|
|||||
| Binaghi, E., Raspanti, M. & Gallo, I. | Neural Disparity Computation for Dense Two-Frame Stereo
Correspondence
[BibTeX] |
2007 | PATTERN RECOGNITION LETTERS | ||
BibTeX:
@article{Binaghi2007neural,
author = {Gallo,, I. and Binaghi,, E. and Raspanti,, M.},
title = {Neural disparity computation for dense two-frame stereo correspondence},
journal = {Pattern Recogn. Lett.},
volume = {29},
number = {5},
year = {2008},
issn = {0167-8655},
pages = {673--687},
doi = {http://dx.doi.org/10.1016/j.patrec.2007.12.003},
publisher = {Elsevier Science Inc.},
address = {New York, NY, USA},
}
|
|||||
| Binaghi, E., Raspanti, M., Gallo, I., Guidali, A. & Salvini, G. | Adaptive Image Restoration of SEM Micrographs:
Preliminary Results
[BibTeX] |
2007 | |||
BibTeX:
@inproceedings{Binaghi2007image,
author = {E. Binaghi and M. Raspanti and I. Gallo and A. Guidali and G. Salvini},
title = {Adaptive Image Restoration of SEM Micrographs: Preliminary Results},
year = {2007},
pages = {?--?}
}
|
|||||
| Boschetti, M., Gallo, I., Brivio, P. & Binaghi, E. | Metodologie per l?analisi delle relazioni tra
caratteristiche ambientali e distribuzione di siti archeologici: mappe
di plausibilit?i sviluppo per l?epoca Romana
[BibTeX] |
2002 | |||
BibTeX:
@inproceedings{Boschetti2002archeologici,
author = {M. Boschetti and I. Gallo and P.A. Brivio and E. Binaghi},
title = {Metodologie per l?analisi delle relazioni tra caratteristiche ambientali e distribuzione di siti archeologici: mappe di plausibilit?i sviluppo per l?epoca Romana},
year = {2002}
}
|
|||||
| Boschetti, M., Gallo, I., Marino, M., Brivio, P. & Binaghi, E. | Retrieval of vegetation understory information fusing
hyperspectral and panchromatic airborne data
[BibTeX] |
2003 | |||
BibTeX:
@inproceedings{Boschetti2003understory,
author = {M. Boschetti and I. Gallo and M. Marino and P.A. Brivio and E. Binaghi},
title = {Retrieval of vegetation understory information fusing hyperspectral and panchromatic airborne data},
year = {2003},
pages = {483--491}
}
|
|||||
| Boschetti, M., Gallo, I., Marino, M., Panigada, C., P., A. B. & Binaghi, E. | Neural Network Techniques Applied to Hyperspectral
Airborne Data to Retrieve Vegetation Understory Information
[BibTeX] |
2004 | |||
BibTeX:
@inproceedings{Boschetti2004understory,
author = {M. Boschetti and I. Gallo and M. Marino and C. Panigada and A. Brivio P. and E. Binaghi},
title = {Neural Network Techniques Applied to Hyperspectral Airborne Data to Retrieve Vegetation Understory Information},
year = {2004}
}
|
|||||
| Brivio, P., Binaghi, E., Gallo, I. & Maggi, M. | Geospatial Pattern Recognition
[BibTeX] |
2002 | |||
BibTeX:
@inbook{Brivio2002burned,
author = {P.A. Brivio and E. Binaghi and I. Gallo and M. Maggi},
title = {Geospatial Pattern Recognition},
publisher = {Research Signpost},
year = {2002},
pages = {189--202}
}
|
|||||
| Brivio, P., Maggi, M., Binaghi, E. & Gallo, I. | Mapping burned surfaces in sub-Saharan Africa based on
multi-temporal neural classification
[BibTeX] |
2003 | INTERNATIONAL JOURNAL OF REMOTE SENSING | ||
BibTeX:
@article{Brivio2003burned,
author = {P.A. Brivio and M. Maggi and E. Binaghi and I. Gallo},
title = {Mapping burned surfaces in sub-Saharan Africa based on multi-temporal neural classification},
journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
year = {2003},
volume = {24},
pages = {4003--4018}
}
|
|||||
| Brivio, P., Maggi, M., Binaghi, E., Gallo, I. & Gregoire, J. | Exploiting spatial and temporal information for
extracting burned areas from time series of SPOT-VGT data
[BibTeX] |
2001 | |||
BibTeX:
@inproceedings{brvio2001burned,
author = {P.A. Brivio and M. Maggi and E. Binaghi and I. Gallo and J.M. Gregoire},
title = {Exploiting spatial and temporal information for extracting burned areas from time series of SPOT-VGT data},
year = {2001},
volume = {2},
pages = {132--139}
}
|
|||||
| Carullo, M., Zanzi, A., Gallo, I. & Coen-Porisini, A. | An Events Synchronization Approach for Integration of
Simulators in a Distributed Environment
[BibTeX] |
2006 | |||
BibTeX:
@inproceedings{Carullo2006events,
author = {M. Carullo and A. Zanzi and I. Gallo and A. Coen-Porisini},
title = {An Events Synchronization Approach for Integration of Simulators in a Distributed Environment},
year = {2006},
pages = {74--78}
}
|
|||||
| Coen-Porisini, A., Gallo, I. & Zanzi, A. | Integration of Web based simulators in the SINPL
platform
[BibTeX] |
2006 | |||
BibTeX:
@inproceedings{coen2006integration,
author = {A. Coen-Porisini and I. Gallo and A. Zanzi},
title = {Integration of Web based simulators in the SINPL platform},
year = {2006},
pages = {259--263}
}
|
|||||
| Coen-Porisini, A., Gallo, I. & Zanzi, A. | Designing and Enacting Simulations using Distributed
Components
[BibTeX] |
2004 | |||
BibTeX:
@inproceedings{coen2004simulations,
author = {A. Coen-Porisini and I. Gallo and A. Zanzi},
title = {Designing and Enacting Simulations using Distributed Components},
year = {2004},
pages = {706--717}
}
|
|||||
| Coen-Porisini, A., Zanzi, A., Colombo, P. & Gallo, I. | A web based solution to manage distributed discrete
event simulations
[BibTeX] |
2007 | |||
BibTeX:
@inproceedings{coen2007web,
author = {A. Coen-Porisini and A. Zanzi and P. Colombo and I. Gallo},
title = {A web based solution to manage distributed discrete event simulations},
year = {2007},
pages = {19--26}
}
|
|||||
| Gallo, I. & Binaghi, E. | Advances in Computer Graphics and Computer Vision | 2007 | DOI | ||
| Abstract: This work aims at defining a new method for matching correspondences in stereoscopic image analysis. The salient aspects of the method are -an explicit representation of occlusions driving the overall matching process and the use of neural adaptive technique in disparity computation. In particular, based on the taxonomy proposed by Scharstein and Szelinsky, the dense stereo matching process has been divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second phase a new strategy has been introduced in an attempt to improve reliability in computing disparity. An experiment was conducted to evaluate the solutions proposed. The experiment is based on an analysis of test images including data with a ground truth disparity map. | |||||
BibTeX:
@inbook{gallo2007dense,
author = {I. Gallo and E. Binaghi},
title = {Advances in Computer Graphics and Computer Vision},
publisher = {Springer Berlin Heidelberg},
year = {2007},
volume = {4},
pages = {343--353},
doi = {10.1007/978-3-540-75274-5_24}
}
|
|||||
| Gallo, I. & Binaghi, E. | Information Extraction and Classification from Free Text Using a NeuralApproach | 2007 | 12th Iberoamerican Congress on Pattern Recognition CIARP 2007 | DOI |
|
| Abstract: Many approaches to Information Extraction (IE) have been proposed in literature capable of finding and extract specific facts in relatively unstructured documents. Their application in a large information space makes data ready for post-processing which is crucial to many context such as Web mining and searching tools. This paper proposes a new IE strategy, based on symbolic and neural techniques, and tests it experimentally within the price comparison service domain. In particular the strategy seeks to locate a set of atomic elements in free text which is preliminarily extracted from web documents and subsequently classify them assigning a class label representing a specific product. | |||||
BibTeX:
@inproceedings{gallo2007ie,
author = {I. Gallo and E. Binaghi},
title = {Information Extraction and Classification from Free Text Using a NeuralApproach},
booktitle = {12th Iberoamerican Congress on Pattern Recognition CIARP 2007},
publisher = {Springer Berlin / Heidelberg},
year = {2007},
volume = {4756/2008},
ISBN = {978-3-540-76724-4},
pages = {921--929},
doi = {10.1007/978-3-540-76725-1_95}
}
|
|||||
| Gallo, I., Binaghi, E. & Macchi, A. | Adaptive Image Restoration using a Local Neural
Approach
[BibTeX] |
2007 | Second International Conference on Computer Vision Theory and Applications VISAPP 2007 | DOI | |
BibTeX:
@inproceedings{gallo2007restoration,
author = {I. Gallo and E. Binaghi and A. Macchi},
title = {Adaptive Image Restoration using a Local Neural Approach},
booktitle = {VISAPP (1): Proceedings of the Second International Conference on Computer Vision Theory and Applications},
year = {2007},
volume = {1},
pages = {161--164}
}
|
|||||
| Gallo, I., Binaghi, E. & Raspanti, M. | Semi-Blind Image Restoration using a Local Neural Approach | 2008 | The IASTED Conference on Signal Processing, Pattern Recognition, and Applications, Innsbruck, Austria |
 |
|
| Abstract: This
work aims to define and experimentally evaluate an iterative strategy
based on neural learning for semi-blind image restoration in the
presence of blur and
noise. A salient aspect of our solution is the local estimation of the restored image based on gradient descent strategies. This method can be viewed as a neural strategy where the pixels of the restored image are the synapse's weights that the neural network tries to modify during learning to minimize the output error measure; the learning strategy adopted is unsupervised. The method was evaluated experimentally using a test pattern generated by a checkerboard function in Matlab. To investigate whether the strategy can be considered an alternative to conventional restoration procedures, the results were compared with those obtained by a well known neural restoration approach. |
|||||
BibTeX:
@inproceedings{gallo2008restoration,
author = {I. Gallo and E. Binaghi and M. Raspanti},
title = {Semi-Blind Image Restoration using a Local Neural Approach},
booktitle = {The IASTED Conference on Signal Processing, Pattern Recognition, and Applications, Innsbruck, Austria},
year = {2008},
pages = {227--231}
}
|
|||||
| Raspanti, M., Binaghi, E., Gallo, I. & Manelli, A. | A vision-based, 3D reconstruction technique for
scanning electron microscopy: Direct comparison with Atomic Force
Microscopy.
[BibTeX] |
2005 | MICROSCOPY RESEARCH AND TECHNIQUE | ||
BibTeX:
@article{Raspanti2005vision,
author = {M. Raspanti and E. Binaghi and I. Gallo and A. Manelli},
title = {A vision-based, 3D reconstruction technique for scanning electron microscopy: Direct comparison with Atomic Force Microscopy.},
journal = {MICROSCOPY RESEARCH AND TECHNIQUE},
year = {2005},
volume = {67},
pages = {1--7}
}
|
|||||
| Abstract: Named Entity Recognition (NER) is an important subtask of document processing such as Information Extraction. This paper describes a NER algorithm which uses a Multi-Layer Perceptron (MLP) to find and classify entities in natural language text. In particular we use the MLP to implement a new supervised context-based NER approach called Sliding Window Neural (SWiN). The SWiN method is a good solution for domains where the documents are grammatically ill-formed and it is difficult to exploit the features derived from linguistic analysis. Experiments indicate good accuracy compared with traditional approaches and demonstrate the system’s portability. | |||||
| Gallo I., Binaghi E., Carullo M. & Lamberti N. | Named Entity Recognition by Neural Sliding Window | 2008 | The Eighth IAPR Workshop on Document Analysis Systems | |
|
BibTeX:
@inproceedings{gallo2008NE,
author = {I. Gallo and E. Binaghi and M. Carullo and N. Lamberti},
title = {Named Entity Recognition by Neural Sliding Window},
booktitle = {The Eighth IAPR Workshop on Document Analysis Systems},
isbn = {978-0-7695-3337-7},
year = {2008},
pages = {567--573},
}
|
|||||
| Abstract: Document clustering techniques have been applied in several areas, with the web as one of the most recent and influent. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. In this work we propose an online, single-pass document clustering model that can be combined with a variety of text-oriented similarity measures. An experimental evaluation of the proposed model was conducted in the e-commerce domain. Per- formances were measured using a clustering-oriented metric based on F-Measure and compared with those obtained by other well-known approaches. | |||||
| Carullo M., Binaghi E., Gallo I. & Lamberti N. | Clustering of Short Commercial Documents for the Web | 2008 | 19th International Conference on Pattern Recognition | |
|
BibTeX:
@inproceedings{carullo2008CLU,
author = {M. Carullo and E. Binaghi and I. Gallo and N. Lamberti},
title = {Clustering of Short Commercial Documents for the Web},
booktitle = {19th International Conference on Pattern Recognition (ICPR)},
isbn = {978-1-4244-2175-6},
year = {2008},
}
|
|||||
| Carullo M., Binaghi E. & Gallo I. | Soft Categorization and Annotation of Images with Radial Basis Function Networks | 2009 | 4th International Conference on Computer Vision Theory and Applications (VISAPP) | |
|
BibTeX:
@inproceedings{carullo2009visapp,
author = {M. Carullo and E. Binaghi and I. Gallo},
title = {Soft Categorization and Annotation of Images with Radial Basis Function Networks},
booktitle = {4th International Conference on Computer Vision Theory and Applications (VISAPP)},
isbn = {978-989-8111-69-2},
year = {2009},
}
|
|||||
| Abstract: This work focuses on fast approaches for image retrieval and classification by employing simple features for the building of image signatures. To this purpose a neural model for soft classification and automatic image annotation is proposed. The salient aspects of this solution are: a) the employment of a Radial Basis Function Network built on top of an image retrieval distance metric b) a soft learning strategy for annotation handling. Experiments have been conducted on a subset of the Corel image dataset for evaluation and comparative analysis. | |||||
| Gallo I. & Binaghi E. | Assigning Automatic Regularization Parameters in Image Restoration | 2009 | 4th International Conference on Computer Vision Theory and Applications (VISAPP) |
|
|
BibTeX:
@inproceedings{gallo2009visapp,
author = {I. Gallo and E. Binaghi},
title = {Assigning Automatic Regularization Parameters in Image Restoration},
booktitle = {4th International Conference on Computer Vision Theory and Applications (VISAPP)},
isbn = {978-989-8111-69-2},
year = {2009},
volume = {1},
pages = {74--77},
editor = {AlpeshKumar Ranchordas and Helder Araujo}
}
|
|||||
| Abstract: This work aims to define and experimentally evaluate an adaptive strategy based on neural learning to select an appropriate regularization parameter within a regularized restoration process. The appropriate setting of the regularization parameter within the restoration process is a difficult task attempting to achieve an optimal balance between removing edge ringing effects and suppressing additive noise. In this context,in an attempt to overcome the limitations of trial and error and curve fitting procedures we propose the construction of the regularization parameter function through a training concept using a Multilayer Perceptron neural network. The proposed solution is conceived independent from a specific restoration algorithm and can be included within a general local restoration procedure. The proposed algorithm was experimentally evaluated and compared using test images with different levels of degradation. Results obtained proven the generalization capability of the method that can be applied successfully on heterogeneous images never seen during training. | |||||
| Binaghi E., Gallo I., Pisani R. & Raspanti M. | An adaptive neural network for the restoration of SEM pictures
[BibTeX] |
2009 | MC 2009, Microscopy Conference | DOI | |
BibTeX:
@inproceedings{binaghi2009mc,
author = {Binaghi E. and Gallo I. and Pisani R. and Raspanti M.},
title = {An adaptive neural network for the restoration of SEM pictures},
booktitle = {MC 2009, Microscopy Conference},
isbn = {978-3-85125-062-6-352},
year = {2009},
}
|
|||||
| Gallo I., Binaghi E. & Raspanti M. | Semi-blind image restoration using a local neural approach | 2009 | Neurocomputing | DOI
| |
BibTeX:
@article{gallo2009neurocomputing,
title = "Semi-blind image restoration using a local neural approach",
journal = "Neurocomputing",
volume = "December 2009",
number = "73",
issues = "1--3",
pages = "389--396",
year = "2009",
month = "August",
issn = "0925-2312",
doi = "DOI: 10.1016/j.neucom.2009.08.001",
url = "http://www.sciencedirect.com/science/article/B6V10-4X1SB8C-6/2/8ad9ce77aeb0ac46c3da6efd5b355479",
author = "Ignazio Gallo and Elisabetta Binaghi and Mario Raspanti",
keywords = "Image restoration",
keywords = "Deconvolution",
keywords = "Adaptive neural network"
}
|
|||||
| Abstract: This work aims to define and experimentally evaluate an iterative strategy based on neural learning for semi-blind image restoration in the presence of blur and noise. Salient aspects of the proposed strategy are the use of a local error function derived from the conventional global constrained error measure and the assignment of a separate regularization parameter to each image pixel based on local gray level variance. This method can be viewed as a neural strategy where the pixels of the restored image are the synapse's weights the neural network tries to modify during learning to minimize the output error measurement. The method was experimentally evaluated in terms of restoration quality and speed using test images synthetically degraded and increasingly corrupted. To investigate whether the strategy can be considered an alternative to neural restoration procedures, the results were compared with those obtained by well known Hopfield-based restoration approaches. Results obtained show that our method performs significantly better and faster than other models considered. | |||||
| Vanetti M. & Gallo I. & Binaghi E. | Dense Two-Frame Stereo Correspondence by Self-Organizing Neural Network | 2009 | Image Analysis and Processing - ICIAP |
DOI
|
|
BibTeX:
@inproceedings{2009iciap,
author = {Vanetti, Marco and Gallo, Ignazio and Binaghi, Elisabetta},
title = {Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network},
booktitle = {ICIAP '09: Proceedings of the 15th International Conference on Image Analysis and Processing},
year = {2009},
isbn = {978-3-642-04145-7},
pages = {1035--1042},
location = {Vietri sul Mare, Italy},
doi = {http://dx.doi.org/10.1007/978-3-642-04146-4_110},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
}
|
|||||
| Abstract: This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self-Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed. | |||||
| E. Binaghi & V. Colli & I. Gallo & S. Strocchi & C. Vite | Neural Adaptive Restoration of Computed Radiography Images | 2009 | World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany |
DOI
|
|
BibTeX:
@inproceedings{2009monaco,
author = {E. Binaghi, V. Colli, I. Gallo, S. Strocchi and C. Vite},
title = {Neural Adaptive Restoration of Computed Radiography Images},
booktitle = { World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany},
year = {2009},
isbn = {978-3-642-03881-5},
pages = {330--333},
location = {Munich, Germany},
doi = {http://dx.doi.org/10.1007/978-3-642-03882-2_87},
publisher = {Springer Berlin Heidelberg}
}
|
|||||
| Abstract: Aim of this work is to experimentally investigate the potential of a novel technique for CR image restoration which make use of gradient descent algorithm to minimize a local error function derived from the conventional global constrained error measure adopted within regularization approaches. Results of preliminary experiments show that the proposed restoration algorithm is promising for medical imaging restoration and could be useful in limiting x-ray dose absorbed by patients. | |||||
| I. Gallo | A Local and Iterative Neural Reconstruction Algorithm for Cone-Beam Data | 2010 | SPIE Medical Imaging, San Diego, California, USA, February 2010 |
DOI
|
|
BibTeX:
@inproceedings{2010sandiego,
author = {Ignazio Gallo},
title = {A Local and Iterative Neural Reconstruction Algorithm for Cone-Beam Data},
booktitle = {Medical Imaging 2010: Physics of Medical Imaging},
year = {2010},
location = {San Diego, California},
series = {Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference},
volume = {7622},
month = {mar},
doi = {10.1117/12.843829},
pages={762253--762262}
}
|
|||||
| Abstract: This work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The main objective of this work is the search of a new reconstruction method, able to work locally, more robust in presence of noisy data and in situations with a small number of projections. This study should be intended as the first step to evaluate the potentialities of the proposed algorithm. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. All the x-rays passing through a cell of the volume to be reconstructed, give origin to a neural network which is a single-layer perceptron network. The network does not need a training set but uses the line integral of a single x-ray as ground-truth of each output neuron. The neural network uses a gradient descent algorithm in order to minimize a local cost function by varying the value of the cells to be reconstructed. The proposed strategy was first evaluated in conditions where the quality and quantity of input data varies widely, using a the Shepp-Logan Phantom. The algorithm was also compared with the iterative ART algorithm and the well known filtered backprojection method. The results show how the proposed algorithm is much more accurate even in the presence of noise and under conditions of lack of data. In situations with little noise the reconstruction, after a few iterations, is almost identical to the original. | |||||
| A. Nodari, E. Binaghi, M. Carullo, and I. Gallo | Key Sample Point Selection: An Improvement of Shape Context Algorithm in Image Retrieval | 2010 | SPIE Medical Imaging, San Diego, California, USA, February 2010 | ||
BibTeX:
@inproceedings{2010SPPRA,
author = {A. Nodari, E. Binaghi, M. Carullo, and I. Gallo },
title = {Key Sample Point Selection: An Improvement of Shape Context Algorithm in Image Retrieval},
booktitle = {Signal Processing, Pattern Recognition and Applications (SPPRA 2010)},
year = {2010},
location = {Innsbruck, Austria},
volume = {678},
month = {February 17 – 19},
}
|
|||||
| Abstract: In this work we defined a new algorithm in the field of Content Based Image Retrieval. The Shape Context Algorithm presents a promising solution to the Shape Analysis problem however its use is strongly limited by the high demand of time and space due to the elevated number of Sample Points required. The new algorithm proposed in this study aims to improve the original Shape Context algorithm’s performance modifying some its relevant parts; furthermore, it was evaluated in term of accuracy, computational time and space. The salient aspects of our algorithm are: a new strategy for the Sample Points selection and a center of mass angle approximation technique in the phase of the shape description computation. We want to reduce the number of Sample Points required by the original algorithm in order to attempt to improve the efficiency in real applications. | |||||
Created by JabRef on 21/09/2008.
Main interests
- Image Processing
- Pattern Recognition
- Neural Computing
- Computer Vision
- Information Extraction (ArTe-Lab)
