149 research outputs found
Interacting regional-scale regime shifts for biodiversity and ecosystem services
Current trajectories of global change may lead to regime shifts at regional scales, driving coupled human–environment systems to highly degraded states in terms of biodiversity, ecosystem services, and human well-being. For business-as-usual socioeconomic development pathways, regime shifts are projected to occur within the next several decades, to be difficult to reverse, and to have regional- to global-scale impacts on human society. We provide an overview of ecosystem, socioeconomic, and biophysical mechanisms mediating regime shifts and illustrate how these interact at regional scales by aggregation, synergy, and spreading processes. We give detailed examples of interactions for terrestrial ecosystems of central South America and for marine and coastal ecosystems of Southeast Asia. This analysis suggests that degradation of biodiversity and ecosystem services over the twenty-first century could be far greater than was previously predicted. We identify key policy and management opportunities at regional to global scales to avoid these shifts
Recurrent blooms of Heterosigma akashiwo (Raphidophyceae) in the Piraquê Channel, Rodrigo de Freitas Lagoon, southeast Brazil
A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images
[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42–49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. 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Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348–1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321–331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502–1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223–230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9–14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784–796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116–123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60–70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829–3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246–253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254–260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261–269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828–3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24–32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383–395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068–4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). 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Guidance on noncorticosteroid systemic immunomodulatory therapy in noninfectious uveitis: fundamentals of care for uveitis (focus) initiative
Topic: An international, expert-led consensus initiative to develop systematic, evidence-based recommendations for the treatment of noninfectious uveitis in the era of biologics.
Clinical Relevance: The availability of biologic agents for the treatment of human eye disease has altered practice patterns for the management of noninfectious uveitis. Current guidelines are insufficient to assure optimal use of noncorticosteroid systemic immunomodulatory agents.
Methods: An international expert steering committee comprising 9 uveitis specialists (including both ophthalmologists and rheumatologists) identified clinical questions and, together with 6 bibliographic fellows trained in uveitis, conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol systematic reviewof the literature (English language studies from January 1996 through June 2016; Medline [OVID], the Central Cochrane library, EMBASE,CINAHL,SCOPUS,BIOSIS, andWeb of Science). Publications included randomized controlled trials, prospective and retrospective studies with sufficient follow-up, case series with 15 cases or more, peer-reviewed articles, and hand-searched conference abstracts from key conferences. The proposed statements were circulated
among 130 international uveitis experts for review.Atotal of 44 globally representativegroupmembersmet in late 2016 to refine these guidelines using a modified Delphi technique and assigned Oxford levels of evidence.
Results: In total, 10 questions were addressed resulting in 21 evidence-based guidance statements covering the following topics: when to start noncorticosteroid immunomodulatory therapy, including both biologic and nonbiologic agents; what data to collect before treatment; when to modify or withdraw treatment; how to select agents based on individual efficacy and safety profiles; and evidence in specific uveitic conditions. Shared decision-making, communication among providers and safety monitoring also were addressed as part of the recommendations. Pharmacoeconomic considerations were not addressed.
Conclusions: Consensus guidelines were developed based on published literature, expert opinion, and practical experience to bridge the gap between clinical needs and medical evidence to support the treatment of patients with noninfectious uveitis with noncorticosteroid immunomodulatory agents
Floristic characterization of an Atlantic Rainforest remnant in Southern Sergipe: Crasto forest
Gestão de competências e qualificação profissional no segmento da alimentação coletiva
"O corpo que eu fui e o corpo que eu sou": concepções de mulheres com alterações causadas pela hanseníase
The corrosion resistance of Wiron®88 in the presence of S. mutans and S. sobrinus bacteria
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