| 1. |
- Perez, Bibiana Gabardo, et al.
(författare)
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Masking ability of resin composites : Effect of the layering strategy and substrate color
- 2022
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Ingår i: Journal of Esthetic and Restorative Dentistry. - : John Wiley & Sons. - 1496-4155 .- 1708-8240. ; 34:8, s. 1206-1212
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Tidskriftsartikel (refereegranskat)abstract
- Objective To evaluate the effect of layering strategy and substrate color on the masking ability of resin composites. Materials and Methods A1-shaded specimens from Charisma Diamond and Filtek Z350XT were produced using different layering strategies. Color measurements were made by a reflectance spectrophotometer over A2, C2, A3.5, C3, C4 substrates. Color differences were calculated and interpreted by the 50%:50% perceptibility and acceptability visual thresholds. Data was analyzed by Kruskal-Wallis and Dunn post hoc test. Chi-square test was used to determine the association between masking ability, and independent variables. Results Color differences were significantly lower on A2 and C2 in comparison with C4 for the majority of the layering strategies. Acceptable matches were observed on most of the combinations over A2. Moderately unacceptable mismatches were observed in most of the combinations over C2 and A3.5. Clearly unacceptable mismatches were observed on the C3 and C4. The Delta E-00 color shifts were predominantly influenced by Delta L-00 for all layering strategies and substrate colors. Conclusion Masking ability was affected by the layering strategy and substrate color. Acceptable masking was associated with A2 and C2, and with layering strategy composed of 0.5 mm enamel opacity and 1.0 mm dentin opacity thicknesses, using the Filtek Z350XT. Clinical Significance Resin composites-shade A1-applied by different layering strategies with a final thickness of 1.5 mm were able to mask mild and moderately discolored substrates. Severely discolored substrates were not masked effectively.
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| 2. |
- Alves, Dimas I, et al.
(författare)
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A Statistical Analysis for Wavelength-Resolution SAR Image Stacks
- 2020
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1545-598X .- 1558-0571. ; 17:2, s. 227-231
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.
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| 3. |
- Alves, Dimas irion, et al.
(författare)
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Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach
- 2023
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Ingår i: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
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| 4. |
- Alves, Dimas I., et al.
(författare)
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Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY
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| 5. |
- Alves, Dimas I, et al.
(författare)
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Statistical Analysis for Wavelength-Resolution SARImage Stacks : New Case Studies
- 2020
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Ingår i: XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS.
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Konferensbidrag (refereegranskat)abstract
- This paper presents new case studies for thestatistical analysis for wavelength resolution SAR image stacks.The statistical analysis considers the Anderson-Darling goodnessof-fit test in a set of pixel samples from the same position obtainedfrom a SAR image stack. The test is applied in wavelengthresolution SAR image stacks. The present work consists of twocase studies based on the use of multiple-pass stacks and TypeI error using the False Discovery Rate controlling procedures.In addition, an application scenario is presented for the studiedscenarios.
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| 6. |
- Alves, Dimas Irion, et al.
(författare)
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Wavelength-Resolution SAR Change Detection Using Bayes' Theorem
- 2020
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Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1939-1404 .- 2151-1535. ; 13, s. 5560-5568
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Tidskriftsartikel (refereegranskat)abstract
- This article presents Bayes' theorem for wavelength-resolution synthetic aperture radar (SAR) change detection method development. Different change detection methods can be derived using Bayes' theorem in combination with the target model, clutter-plus-noise model, iterative implementation, and noniterative implementation. As an example of the Bayes' theorem use for wavelength-resolution SAR change detection method development, we propose a simple change detection method with a clutter-plus-noise model and noniterative implementation. In spite of simplicity, the proposed method provides a very competitive performance in terms of probability of detection and false alarm rate. The best result was a probability of detection of $\text{98.7}\%$ versus a false alarm rate of one per square kilometer.
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| 7. |
- Berner, Jessica, et al.
(författare)
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Five-factor model, technology enthusiasm and technology anxiety
- 2023
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Ingår i: Digital Health. - : Sage Publications. - 2055-2076. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online.
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| 8. |
- da Silva, Fabiano G., et al.
(författare)
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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
- 2022
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13
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Tidskriftsartikel (refereegranskat)abstract
- This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 9. |
- da Silva, Fabiano G., et al.
(författare)
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Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery
- 2022
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering.
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Konferensbidrag (refereegranskat)abstract
- Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.
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| 10. |
- da Silva, Fabiano Gabriel, et al.
(författare)
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Hyperparameters Analysis of Machine Learning Techniques for Classification of Marine Targets in SAR Images
- 2023
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Ingår i: Proceedings of the XX Brazilian Symposium on Remote Sensing. - 9786589159049 ; , s. 1095-1098
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Konferensbidrag (refereegranskat)abstract
- Due to the extensive coastal area of Brazil, pattern recognition techniques based on artificial intelligence can search for targets at sea faster for surveillance, rescue, or illicit combat activities. This article presents a hyperparameter analysis of machine learning techniques to classify targets in SAR images. We considered a data set with vertical horizontal polarization SAR images from Campos Basin, Rio de Janeiro, to classify oil platforms and ships. The classification attributes are extracted through a convolutional neural network VGG-16 pre-trained with the ImageNet data set. Then, four machine learning techniques are evaluated, random forest, decision tree, k-nearest-neighbors, and logistic regression. As a metric for assessing the classifiers, accuracy (Acc) and area under the curve (AUC) are used. The grid search technique is used to identify the best combination of parameters of the classifiers with the highest Acc and AUC. Finally, the best result is the logistic regression classifier.
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