Dr. Rodolfo A. Fiorini Profile

Dr. Rodolfo A. Fiorini

Professor at Politecnico di Milano

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Publications (8)

Proceedings Article | 28 March 2005 Paper

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

Rodolfo Fiorini, Gianfranco Dacquino

Proceedings Volume 5812, (2005) https://doi.org/10.1117/12.603560

KEYWORDS: Pattern recognition, Visualization, Melanoma, Image classification, Optical spheres, Matrices, 3D modeling, Associative arrays, Image processing, Image visualization

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GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Proceedings Article | 8 December 2004 Paper

n-D shape/texture optimal synthetic description and modeling by GEOGINE

Rodolfo Fiorini, Gianfranco Dacquino

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.578546

KEYWORDS: Transform theory, Vector spaces, Clouds, Associative arrays, Melanoma, Mathematical modeling, Visualization, Pattern recognition, Skin, 3D modeling

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Proceedings Article | 6 August 2002 Paper

Tensor invariant model for target discrimination

Gianfranco Dacquino, Paolo Aschedamini, Rodolfo Fiorini, Antonio Meroni

Proceedings Volume 4718, (2002) https://doi.org/10.1117/12.478797

KEYWORDS: Image processing, Transform theory, Mathematical modeling, Target recognition, Stochastic processes, Databases, Image classification, Visualization, Biomedical engineering, 3D image processing

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Proceedings Article | 30 October 1997 Paper

DELM image processing for skin-melanoma early diagnosis

Rodolfo Fiorini, M. Crivellini, G. Codagnone, Gianfranco Dacquino, M. Libertini, A. Morresi

Proceedings Volume 3164, (1997) https://doi.org/10.1117/12.279562

KEYWORDS: Skin, Image processing, Melanoma, Imaging systems, Computing systems, Tumors, Digital cameras, Cameras, Control systems, Signal to noise ratio

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Among the various skin diseases skin tumors are the most serious ones and skin Melanoma is particularly dangerous. Its malignant evolution lasts about 5 or 6 years and ends with the death of the patient. Early diagnosis is a powerful means of preventing this evolution allowing sudden intervention, which increases probability or recover and survival. Aim of the paper is to present the result of an active support system for early diagnosis of melanoma and related skin diseases. The system is based upon a digital acquisition camera with a dedicated illumination system digitally controlled in order to achieve best performance in color and feature discrimination reaching best signal to noise ratio especially in blue band. A polarization framework allows for reflected ray rejection maximization. A new classification approach is presented. It allows for a quantification of morphological patterns and standard parameters in order to implement a computer aided dermatological system. The image information extraction is based on minimal descriptor set of parameters in order to classify chromatic texture and morphological features. The results obtained allow for determination of standard reference grids for pathological cases and reliable and objective classification procedure. We adopt, as reference, the approach used by Stanganelli and Kenet. Through a bioengineering analysis we can organize reference grids that offer the possibility to extract the maximum information content from dermatological data. The classification takes into account the spread and intrinsic descriptors and correspond to the best operative description. Therefore these grids are the more suitable tools for applications which requires active support system for diagnosis. In fact it is possible to obtain quantitative evaluations too. We propose a method based on geometrical synthetical descriptors. All that permits a reliable early diagnosis of melanotic disease and to follow its evolution in time. The first result and the incoming work points to the realization of a system for general dermatological applications.

Proceedings Article | 29 October 1997 Paper

Domain sensor: a new device for target detection and acquisition

Rodolfo Fiorini, Gianfranco Dacquino

Proceedings Volume 3163, (1997) https://doi.org/10.1117/12.283973

KEYWORDS: Sensors, Target detection, Measurement devices, Active sensors

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Proceedings Article | 16 June 1997 Paper

Skin melanoma tracking by DELM active support system

Rodolfo Fiorini, M. Crivellini, G. Codagnone, Gianfranco Dacquino, G. De Biase, M. Libertini, A. Morresi, G. Testa

Proceedings Volume 2976, (1997) https://doi.org/10.1117/12.275523

KEYWORDS: Melanoma, Skin, Colorimetry, Image quality, Reflectivity, Imaging systems, Optical fibers, Diagnostics, Tissue optics, Standards development

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Proceedings Article | 25 April 1997 Paper

Effective chromatic texture coding for robust skin disease minimal descriptor quantification

Rodolfo Fiorini, M. Crivellini, G. Codagnone, Gianfranco Dacquino, M. Libertini, A. Morresi

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274156

KEYWORDS: Skin, Melanoma, Colorimetry, Diagnostics, Digital imaging, Reflectivity, Image quality, Imaging systems, Tumors, Optical fibers

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Among the various skin diseases skin tumors are the most serious ones and skin melanoma is particularly dangerous. Its malignant evolution lasts about 5 or 6 years and ends with the death of the patient. Early diagnosis is a powerful means of preventing this evolution allowing sudden intervention which increases probability of recover and survival. Purpose of this paper is to present an active support system (ASS) able to reveal and quantify the stage of disease evolution. The work focuses the problem encountered in chromatic information encoding the morphological aspects quantification. A new method is proposed which permits robust and reliable quantification of image data obtained via a digital epiluminescence dermatoscopy apparatus (DELM) designed and built with interesting new features. The image information extraction is based on minimal descriptor set of parameters in order to classify chromatic texture and morphological features. The active support systems is based on DELM technique, taking advantage of polarized light guided by optical fibers. In the purpose to discriminate between malignant and benign melanocytic lesions, several dermatoscopical features have been proposed by different research groups. Nevertheless many are the attempts to reach a reliable and objective classification procedure. We adopt, as reference, the approach used by Stanganelli and Kenet. Through a bioengineering analysis we can organize reference grids that offer the possibility to extract the maximum information content from dermatological data. The classification takes into account the Spread and Intrinsic Descriptors and correspond to the best operative description. Therefore these grids are the more suitable tools for application which require ASS for diagnosis. In fact it is possible to obtain quantitative evaluations too. We propose a method based on geometrical synthetical descriptors. All that permits a reliable early diagnosis of melanotic disease and to follow its evolution in time. The results obtained allow for disease classification procedure with determination of reference grids for pathological cases and ultimately permits effective early diagnosis of melanotic disease and its follow-up. The first results and the incoming work points to the realization of an Automatic Support System for general dermatological applications.3034

Proceedings Article | 9 October 1995 Paper

Numerical fovea: an efficient solution to discrete inverse problems

Gianfranco Dacquino, Rodolfo Fiorini, B. Cattaneo, A. Fabiani

Proceedings Volume 2570, (1995) https://doi.org/10.1117/12.224180

KEYWORDS: Head, Sensors, Eye, Inverse problems, Visualization, Receptors, Visual system, Mathematical modeling, Retina, Cameras

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