5,169 research outputs found
Il-fonestetika ta’ Mario Azzopardi
No full textF’dan l-istudju nistħarreġ l-element fonetiku fil-versi alliterattivi ta’ Mario Azzopardi u l-istrateġiji li jinqdew bihom. L-għan huwa li nesplora s-sens u l-atmosfera li jinħolqu minn insistenza fuq ħsejjes ripetittivi partikolari f’relazzjoni mat-tifsir. Il-poeżiji ta’ Azzopardi sikwit huma kkaratterizzati minn kakofonija, dissonanza jew ħsejjes diżarmonjużi, mekkaniżmi li jiksbu relevanza figurattiva, bħal filkaż ta’ “vjolin iħanxar” (“notturn” minn Analiżi ’70); “il-psike tiegħi jżarżar bħal terramaxka” (“varjazzjoni fuq l-imħabba”, minn Le Poète); “melodija mibruxa” u “l-kampnar tal-knisja / jqanpen agunija” (“suite 345” minn Analiżi ’70); u “bil-vokabularju jfaqqa’ / bħal ċuqlajta tal-għadam” (“Il-Manifest tal-Poeżija” minn Il-Fabbrikant tal-Marjunetti). L-allużjonijiet figurattivi għall-ħoss f’dawn l-eżempji jirriflettu l-fonestetika ta’ diskordanza. Dawn ir-riflessjonijiet iservu bħala punt tat-tluq għal analiżi li se tiffoka fuq ir-relazzjoni bejn il-fonemi b’karatteristiċi partikolari, simili jew kuntrastanti bejniethom. B’dan il-mod, nuri kif il-tqegħid tal-fonemi fi gruppi jista’ jaqdi wkoll funzjoni simbolika.peer-reviewe
Contro la funzionalizzazione della contrattazione collettiva. Riflessioni sul pensiero di Mario Rusciano
No full textL'autore riflette sul pensiero di Mario Rusciano in punto di funzionalizzazione della contrattazione collettiva.The author reflects on the thought of Mario Rusciano in relation to the subject of the functionalisation of collective bargaining
Pariġi
No full textĠabra ta’ poeżiji u proża li tinkludi: Bir ta’ Achille Mizzi – Tixjin ta’ Achille Mizzi – Arlekkin ta’ Achille Mizzi – Il-fanal ta’ George Zammit – Nida ta’ Ġorġ Borg – Lampara ta’ John Buttigieg – Tema bil-kompjuter ta’ Mario Azzopardi – Ardabjola ta’ Mario Azzopardi – Il-biża’ tal-Griegi ta’ Frans Sammut – Pariġi ta’ Oliver Friggieri.peer-reviewe
FaiResGAN: Fair and robust blind face restoration with biometrics preservation
No full textModern computer vision technologies enable systems to detect, recognize, and analyze facial features, but challenges arise when images are noisy, blurred, or low quality. Blind face restoration, which aims to recover high-quality facial images without prior knowledge of degradation, addresses this issue. In this paper, we introduce Fair Restoration GAN (FaiResGAN), a novel Generative Adversarial Network (GAN) designed to balance face restoration with the preservation of soft biometrics (identity, ethnicity, age, and gender). Our model incorporates a pseudo-random batch composition algorithm to promote fairness and mitigate bias, alongside a realistic degradation model simulating corruptions typical in surveillance images. Experimental results show that FaiResGAN outperforms state-of-the-art blind face restoration methods, both quantitatively and qualitatively. A user study involving 40 participants showed that FaiResGAN-restored images were preferred by 70% of users. Additionally, tests on VGGFace2, UTKFace, and FairFace datasets demonstrate FaiResGAN's superior performance in preserving soft biometric attributes and ensuring fair restoration across different genders and ethnicities
Gender recognition from face images using a fusion of SVM classifiers
No full textThe recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote.We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6%, which outperforms the commercial products Face++ and Luxand
Fusion of domain-specific and trainable features for gender recognition from face images
No full textThe popularity and the appeal of systems which are able to automatically determine the gender from face images is growing rapidly. Such a great interest arises from the wide variety of applications, especially in the fields of retail and video surveillance. In recent years there have been several attempts to address this challenge, but a definitive solution has not yet been found. In this paper we propose a novel approach that fuses domain-specific and trainable features to recognize the gender from face images. In particular, we use the SURF descriptors extracted from 51 facial landmarks related to eyes, nose and mouth as domain dependent features, and the COSFIRE filters as trainable features. The proposed approach turns out to be very robust with respect to the well known face variations, including different poses, expressions and illumination conditions. It achieves state-of-the-art recognition rates on the GENDER- FERET (94.7%) and on the LFW (99.4%) datasets, which are two of the most popular benchmarks for gender recognition. We further evaluated the method on a new dataset acquired in real scenarios, the UNISA- Public, recently made publicly available. It consists of 206 training (144 male, 62 female) and 200 test (139 male, 61 female) images that are acquired with a real-time indoor camera capturing people in regular walking motion. Such experiment has the aim to assess the capability of the algorithm to deal with face images extracted from videos, which are definitely more challenging than the still images available in the standard datasets. Also for this dataset we achieved a high recognition rate of 91.5%, that confirms the generalization capabilities of the proposed approach. Of the two types of features, the trainable COSFIRE filters are the most effective and, given their trainable character, they can be applied in any visual pattern recognition problem
Lill-Madonna tas-saħħa
No full textĠabra ta’ poeżiji u proża li tinkludi: Siġar ta’ Dun Frans Camilleri – Umanità ta’ Ġużè Chetcuti – Lil ommi b’qima ta’ Mario Agius – Il-kummiedja ta’ Ger. Azzopardi – Bejn art u oħra ta’ V. M. Pellegrini – Lill-Madonna tas-saħħa ta’ Vincent Ungaro.N/
Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks
No full textSupervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters
No full textThe inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vessel-endings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach
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