3,217 research outputs found
An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits
No full textSiracusa, Laura, Onofri, Andrea, Galesi, Rosario, Impelluso, Carmen, Pulvirenti, Luana, Ruberto, Giuseppe, Gresta, Fabio, Spampinato, Giovanni, Cristaudo, Antonia (2022): An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits. Phytochemistry (113315) 202: 1-11, DOI: 10.1016/j.phytochem.2022.113315, URL: http://dx.doi.org/10.1016/j.phytochem.2022.11331
Fast and accurate computation of orthogonal moments for texture analysis
No full textIn this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community's disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification
Comparison of statistical features for medical colour image classification
No full textAnalysis of cells and tissues allow the evaluation and diagnosis of a vast number of diseases. Nowadays this analysis is still performed manually, involving numerous drawbacks, in particular the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the sample and provides precise results. In this work we investigate different texture descriptors extracted from colour medical images. We compare and combine these features in order to identify the features set able to properly classify medical images presenting different classification problems. The tested feature sets are based on a generalization of some existent grey scale approaches for feature extraction to colour images. The generalization has been applied to the calculation of Grey-Level Co-Occurrence Matrix, Grey-Level Difference Matrix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases, HistologyDS, Pap-smear and Lymphoma, that present different medical problems and so they represent different classification problems. The obtained experimental results have showed that the features extracted from the generalized Grey-Level Co-Occurrence Matrix perform better than the other set of features, demonstrating also that a combination of features selected from all the feature subsets leads always to better performances
On different colour spaces for medical colour image classification
No full textAnalysis of cells and tissues allow the evaluation and diagnosis of a vast number of diseases. Nowadays this analysis is still performed manually, involving numerous drawbacks, in particular the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the sample and provides precise results. In this work we investigate different texture descriptors extracted from medical images in different colour spaces. We compare these features in order to identify the features set able to properly classify medical images presenting different classification problems. Furthermore, we investigate different colour spaces to identify most suitable for this purpose. The feature sets tested are based on a generalization of some existent grey scale approaches for feature extraction to colour images. The generalization has been applied to the calculation of Grey-Level Co-Occurrence Matrix, Grey-Level Difference Matrix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases, HistologyDS, Pap-smear and Lymphoma, that present different medical problems and so they represent different classification problems. The obtained experimental results have showed that in general features extracted from the HSV colour space perform better than the other and that the best feature subset has been obtained from the generalized Grey-Level Co-Occurrence Matrix, demonstrating excellent performances for this purpose
Evaluation of Statistical Features for Medical Image Retrieval
No full textIn this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics
of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa-
chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy
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Trust in authorities monitoring the distribution of genetically modified foods: dimensionality, measurement issues, and determinants
No full textBased on a combined internet and mail survey in Germany the independence of indica-tors of trust in public authorities from indicators of attitudes toward genetically modified food is tested. Despite evidence of a link between trust indicators on the one hand and evaluation of benefits and perceived likelihoods of risks, correlation with other factors is found to be moderate on average. But the trust indicators exhibit only a moderate relation with the re-spondents’ preference for either sole public control or a cooperation of public and private bodies in the monitoring of GM food distribution. Instead, age and location in either the New or the Old Lander are found to be significantly related with such preferences
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A critical account of the relationship between institutional trust, risk perception, and technology acceptance with an application to genetically modified foods
No full textThis article critically reflects on the widely held view of a causal chain with trust in public authorities impacting technology acceptance via perceived risk. It first puts forward conceptual reason against this view, as the presence of risk is a precondition for trust playing a role in decision making. Second, results from consumer surveys in Italy and Germany are presented that support the associationist model as counter hypothesis. In that view, trust and risk judgments are driven by and thus simply indicators of higher order attitudes toward a certain technology which determine acceptance instead. The implications of these findings are discussed
Carta particolare delle 6 Isole de Molucchi [cartographic material] : d'Asia carta XIII, L°.6° /
No full textSecond ed. Map of the Moluccas Islands.; Plate [88] from: Dell'arcano del mare, di D. Ruberto Dudleo Duca di Nortumbria, e conte di vvarvich, libri Sei ... Vol. 3, bk 6. 1661.; "La longitune cominca da l'Isola di Pico d 'Asores"'.; The A.E. Nordenskiold collection in the Helsinki University Library ... / comp. by Ann-mari Mickwitz and Leena Miekkavaara. [Helsinki] : Helsinki University Library, 1979, vol.1, p. 146.; Catalogue of the Library / National Maritime Museum. London : H.M.S.O., 1979, vol.3, part 1, p.387.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.map-rm1837
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